Data Science Blog

OpenAI models have transformed the landscape of artificial intelligence, redefining what’s possible in natural language processing, machine learning, and generative AI. From the early days of GPT-1 to the groundbreaking capabilities of GPT-5, each iteration has brought significant advancements in architecture, training data, and real-world applications.

In this comprehensive guide, we’ll explore the evolution of OpenAI models, highlighting the key changes, improvements, and technological breakthroughs at each stage. Whether you’re a data scientist, AI researcher, or tech enthusiast, understanding this progression will help you appreciate how far we’ve come and where we’re headed next.

GPT-1 (2018) – The Proof of Concept

The first in the series of OpenAI models, GPT-1, was based on the transformer models architecture introduced by Vaswani et al. in 2017. With 117 million parameters, GPT-1 was trained on the BooksCorpus dataset (over 7,000 unpublished books), making it a pioneer in large-scale unsupervised pre-training.

Technical Highlights:

• Architecture: 12-layer transformer decoder.
• Training Objective: Predict the next word in a sequence (causal language modeling).
• Impact: Demonstrated that pre-training on large text corpora followed by fine-tuning could outperform traditional machine learning models on NLP benchmarks.

While GPT-1’s capabilities were modest, it proved that scaling deep learning architectures could yield significant performance gains.

GPT-2 (2019) – Scaling Up and Raising Concerns

GPT-2 expanded the GPT architecture to 1.5 billion parameters, trained on the WebText dataset (8 million high-quality web pages). This leap in scale brought dramatic improvements in natural language processing tasks.

Key Advancements:

• Longer Context Handling: Better at maintaining coherence over multiple paragraphs.
• Zero-Shot Learning: Could perform tasks without explicit training examples.
• Risks: OpenAI initially withheld the full model due to AI ethics concerns about misuse for generating misinformation.

Architectural Changes:

• Increased depth and width of transformer layers.
• Larger vocabulary and improved tokenization.
• More robust positional encoding for longer sequences.

This was the first time OpenAI models sparked global debate about responsible AI deployment — a topic we cover in Responsible AI with Guardrails.

GPT-3 (2020) – The 175 Billion Parameter Leap

GPT-3 marked a paradigm shift in large language models, scaling to 175 billion parameters and trained on a mixture of Common Crawl, WebText2, Books, and Wikipedia.

Technological Breakthroughs:

• Few-Shot and Zero-Shot Mastery: Could generalize from minimal examples.
• Versatility: Excelled in translation, summarization, question answering, and even basic coding.
• Emergent Behaviors: Displayed capabilities not explicitly trained for, such as analogical reasoning.

Training Data Evolution:

• Broader and more diverse datasets.
• Improved filtering to reduce low-quality content.
• Inclusion of multiple languages for better multilingual performance.

However, GPT-3 also revealed challenges:

• Bias and Fairness: Reflected societal biases present in training data.
• Hallucinations: Confidently generated incorrect information.
• Cost: Training required massive computational resources.

For a deeper dive into LLM fine-tuning, see our Fine-Tune, Serve, and Scale AI Workflows guide.

Codex (2021) – Specialization for Code

Codex was a specialized branch of OpenAI models fine-tuned from GPT-3 to excel at programming tasks. It powered GitHub Copilot and could translate natural language into code.

Technical Details:

• Training Data: Billions of lines of code from public GitHub repositories, Stack Overflow, and documentation.
• Capabilities: Code generation, completion, and explanation across multiple languages (Python, JavaScript, C++, etc.).
• Impact: Revolutionized AI applications in software development, enabling rapid prototyping and automation.

Architectural Adaptations:

• Fine-tuning on code-specific datasets.
• Adjusted tokenization to handle programming syntax efficiently.
• Enhanced context handling for multi-file projects.

Explore the top open-source tools powering the new era of agentic AI in this detailed breakdown.

GPT-3.5 (2022) – The Conversational Bridge

GPT-3.5 served as a bridge between GPT-3 and GPT-4, refining conversational abilities and reducing latency. It powered the first public release of ChatGPT in late 2022.

Improvements Over GPT-3:

• RLHF (Reinforcement Learning from Human Feedback): Improved alignment with user intent.
• Reduced Verbosity: More concise and relevant answers.
• Better Multi-Turn Dialogue: Maintained context over longer conversations.

Training Data Evolution:

• Expanded dataset with more recent internet content.
• Inclusion of conversational transcripts for better dialogue modeling.
• Enhanced filtering to reduce toxic or biased outputs.

Architectural Enhancements:

• Optimized inference for faster response times.
• Improved safety filters to reduce harmful outputs.
• More robust handling of ambiguous queries.

GPT-4 (2023) – Multimodal Intelligence

GPT-4 represented a major leap in generative AI capabilities. Available in 8K and 32K token context windows, it could process and generate text with greater accuracy and nuance.

Breakthrough Features:

• Multimodal Input: Accepted both text and images.
• Improved Reasoning: Better at complex problem-solving and logical deduction.
• Domain Specialization: Performed well in law, medicine, and finance.

Architectural Innovations:

• Enhanced attention mechanisms for longer contexts.
• More efficient parameter utilization.
• Improved safety alignment through iterative fine-tuning.

We explored GPT-4’s enterprise applications in our LLM Data Analytics Agent Guide.

See how GPT-3.5 and GPT-4 stack up in reasoning, accuracy, and performance in this head-to-head comparison.

GPT-4.1 (2025) – High-Performance Long-Context Model

Launched in April 2025, GPT-4.1 and its mini/nano variants deliver massive speed, cost, and capability gains over earlier GPT-4 models. It’s built for developers who need long-context comprehension, strong coding performance, and responsive interaction at scale.

Breakthrough Features:

• 1 million token context window: Supports ultra-long documents, codebases, and multimedia transcripts.

• Top-tier coding ability: 54.6% on SWE-bench Verified, outperforming previous GPT-4 versions by over 20%.

• Improved instruction following: Higher accuracy on complex, multi-step tasks.

• Long-context multimodality: Stronger performance on video and other large-scale multimodal inputs.

Get the full scoop on how the GPT Store is transforming AI creativity and collaboration in this launch overview.

Technological Advancements:

• 40% faster & 80% cheaper per query than GPT-4o.

• Developer-friendly API with variants for cost/performance trade-offs.

• Optimized for production — Balances accuracy, latency, and cost in real-world deployments.

GPT-4.1 stands out as a workhorse model for coding, enterprise automation, and any workflow that demands long-context precision at scale.

GPT-OSS (2025) – Open-Weight Freedom

OpenAI’s GPT-OSS marks its first open-weight model release since GPT-2, a major shift toward transparency and developer empowerment. It blends cutting-edge reasoning, efficient architecture, and flexible deployment into a package that anyone can inspect, fine-tune, and run locally.

Breakthrough Features:

• Two model sizes: gpt-oss-120B for state-of-the-art reasoning and gpt-oss-20B for edge and real-time applications.

• Open-weight architecture: Fully released under the Apache 2.0 license for unrestricted use and modification.

• Advanced reasoning: Supports full chain-of-thought, tool use, and variable “reasoning effort” modes (low, medium, high).

• Mixture-of-Experts design: Activates only a fraction of parameters per token for speed and efficiency.

Technological Advancements:

• Transparent safety: Publicly documented safety testing and adversarial evaluations.

• Broad compatibility: Fits on standard high-memory GPUs (80 GB for 120B; 16 GB for 20B).

• Benchmark strength: Matches or exceeds proprietary OpenAI reasoning models in multiple evaluations.

By giving developers a high-performance, openly available LLM, GPT-OSS blurs the line between cutting-edge research and public innovation.

Uncover how GPT-OSS is reshaping the AI landscape by bringing open weights to the forefront in this comprehensive overview.

GPT-5 (2025) – The Next Frontier

The latest in the OpenAI models lineup, GPT-5, marks a major leap in AI capability, combining the creativity, reasoning power, efficiency, and multimodal skills of all previous GPT generations into one unified system. Its design intelligently routes between “fast” and “deep” reasoning modes, adapting on the fly to the complexity of your request.

Breakthrough Features:

• Massive context window: Up to 256K tokens in ChatGPT and up to 400K tokens via the API, enabling deep document analysis, extended conversations, and richer context retention.

• Advanced multimodal processing: Natively understands and generates text, interprets images, processes audio, and supports video analysis.

• Native chain-of-thought reasoning: Delivers stronger multi-step logic and more accurate problem-solving.

• Persistent memory: Remembers facts, preferences, and context across sessions for more personalized interactions.

Technological Advancements:

• Intelligent routing: Dynamically balances speed and depth depending on task complexity.

• Improved zero-shot generalization: Adapts to new domains with minimal prompting.

• Multiple variants: GPT-5, GPT-5-mini, and GPT-5-nano offer flexibility for cost, speed, and performance trade-offs.

GPT-5’s integration of multimodality, long-context reasoning, and adaptive processing makes it a truly all-in-one model for enterprise automation, education, creative industries, and research.

Discover everything about GPT-5’s features, benchmarks, and real-world use cases in this ultimate guide.

Comparing the Evolution of OpenAI Models

Explore the top eight custom GPTs for data science on the GPT Store and discover which ones could supercharge your workflow.

Technological Trends Across OpenAI Models

1. Scaling Laws in Deep Learning

   Each generation has exponentially increased in size and capability.

2. Multimodal Integration

   Moving from text-only to multi-input processing.

3. Alignment and Safety

   Increasing focus on AI ethics and responsible deployment.

4. Specialization

   Models like Codex show the potential for domain-specific fine-tuning.

The Role of AI Ethics in Model Development

As OpenAI models have grown more powerful, so have concerns about bias, misinformation, and misuse. OpenAI has implemented reinforcement learning from human feedback and content moderation tools to address these issues.

For a deeper discussion, see our Responsible AI Practices article.

Future Outlook for OpenAI Models

Looking ahead, we can expect:

• Even larger machine learning models with more efficient architectures.
• Greater integration of AI applications into daily life.
• Stronger emphasis on AI ethics and transparency.
• Potential for real-time multimodal interaction.

Conclusion

The history of OpenAI models is a story of rapid innovation, technical mastery, and evolving responsibility. From GPT-1’s humble beginnings to GPT-5’s cutting-edge capabilities, each step has brought us closer to AI systems that can understand, reason, and create at human-like levels.

For those eager to work hands-on with these technologies, our Large Language Bootcamp and Agentic AI Bootcamp offers practical training in natural language processing, deep learning, and AI applications.

On August 7, 2025, OpenAI officially launched GPT‑5, its most advanced and intelligent AI model to date. GPT-5 now powers popular platforms like ChatGPT, Microsoft Copilot, and the OpenAI API. This release is a major milestone in artificial intelligence, offering smarter reasoning, better coding, and easier access for everyone—from everyday users to developers. In this guide, we’ll explain what makes GPT-5 unique, break down its new features in simple terms, and share practical, step-by-step tips for getting started—even if you’re brand new to AI.

The open-source AI revolution is here. Learn how GPT OSS is changing the game by making powerful language models more accessible to everyone.

What’s New in GPT-5?

1. A Smarter, Unified System

GPT‑5 uses a multi‑model architecture—imagine it as a team of experts working together to answer your questions.

• Fast, Efficient Model:

  For simple questions (like “What’s the capital of France?”), it uses a lightweight model that responds instantly.

• Deep Reasoning Engine (“GPT‑5 thinking”):

  For complex tasks (like solving math problems, writing code, or analyzing long documents), it switches to a more powerful “deep thinking” mode for detailed, accurate answers.

• Real-Time Model Routing:

  GPT-5 automatically decides which expert to use for each question. If you want deeper analysis, you can add phrases like “think step by step” or “explain your reasoning” to your prompt.

• User Control:

  Advanced users and developers can adjust settings to control how much effort GPT-5 puts into answering. Beginners can simply type their question and let GPT-5 do the work.

Sample Prompt for Beginners:

• “Explain how photosynthesis works, step by step.”
• “Think carefully and help me plan a weekly budget.”

Want to get even better answers from GPT-5? Discover the art of context engineering

2. Expanded Context Window

What’s a context window?

Think of GPT-5’s memory as a giant whiteboard. The context window is how much information it can see and remember at once.

• API Context Capacity:

  It can process up to 400,000 tokens. For beginners, a “token” is roughly ¾ of a word. So, GPT-5 can handle about 300,000 words at once—enough for an entire book or a huge code file.

• Other Reports:

  Some sources mention smaller or larger windows, but 400,000 tokens is the official figure.

• Why It Matters:

  GPT-5 can read, remember, and respond to very long documents, conversations, or codebases without forgetting earlier details.

Beginner Analogy:

If you’re chatting with GPT-5 about a 500-page novel, it can remember the whole story and answer questions about any part of it.

Sample Use:

• Paste a long article or contract and ask, “Summarize the key points.”
• Upload a chapter from a textbook and ask, “What are the main themes?”

Ever wondered what’s happening under the hood? Our beginner-friendly guide on how LLMs work breaks down the science behind models like GPT-5 in simple terms.

3. Coding, Reasoning & Tool Use

GPT‑5 is a powerful assistant for learning, coding, and automating tasks—even if you’re just starting out.

• Coding Benchmarks:

  GPT-5 is top-rated for writing and fixing code, but you don’t need to be a programmer to benefit.

• Tool Chaining:

  GPT-5 can perform multi-step tasks, like searching for information, organizing it, and creating a report—all in one go.

• Customizable Prompting:

  You can ask for short answers (“Keep it brief”) or detailed explanations (“Explain in detail”). Use the reasoning_effort setting for more thorough answers, but beginners can just ask naturally.

Make coding feel effortless. Discover Vibe Coding, a fun, AI-assisted way to turn your ideas into working code—no stress required.

Sample Prompts for Beginners:

• “Write a simple recipe for chocolate cake.”
• “Help me organize my weekly schedule.”
• “Find the main idea in this paragraph: [paste text].”

Step-by-Step Example:

1. Paste your question or text.
2. Ask GPT-5 to “explain step by step” or “show all the steps.”
3. Review the answer and ask follow-up questions if needed.

4. Multimodal & Enhanced Safety

GPT‑5 isn’t limited to text—it can work with images, audio, and video, and is designed to be safer and more reliable.

Explore multimodality in LLMs to see how models like GPT-5 understand and work across multiple formats.

• Multimodal Input:

  You can upload a photo, audio clip, or video and ask GPT-5 to describe, summarize, or analyze it.

• How to Use (Step-by-Step):

  1. In ChatGPT or Copilot, look for the “upload” button.
  2. Select your image or audio file.
  3. Type a prompt like “Describe this image” or “Transcribe this audio.”
  4. GPT-5 will respond with a description or transcription.

• Integration with Apps:

  It connects with Gmail, Google Calendar, and more, making it easy to automate tasks or get reminders.

• Improved Safety:

  GPT-5 is less likely to make up facts (“hallucinate”) and is designed to give more accurate, trustworthy answers—even for sensitive topics.

Beginner Tip:

Always double-check important information, especially for health or legal topics. Use GPT-5 as a helpful assistant, not a replacement for expert advice.

Wondering how far we’ve come before GPT-5? Check out our GPT-3.5 vs GPT-4 comparison

5. Available Variants & Pricing

GPT‑5 offers different versions to fit your needs and budget.

• Standard:

  Full-featured model for most tasks.

• Mini and Nano:

  Faster, cheaper versions for quick answers or high-volume use.

• Pro Tier in ChatGPT:

  Unlocks advanced features like “GPT‑5 Thinking” for deeper analysis.

• Getting Started for Free:

  • You can use GPT-5 for free with usage limits on platforms like ChatGPT and Copilot.
  • For more advanced or frequent use, consider upgrading to a paid plan.
  • Pricing is flexible—start with the free tier and upgrade only if you need more power or features.

Beginner Tip:

Try GPT-5 for free on ChatGPT or Copilot. No coding required—just type your question and explore!

Want AI that can search, think, and act on its own? Learn how Agentic RAG combines retrieval and agentic capabilities for powerful, autonomous problem-solving.

Summing It Up

GPT-5 is smarter, remembers more, codes better, and interacts in new ways. Here’s a simple comparison:

Want AI that thinks in layers, like humans? Dive into the Hierarchical Reasoning Model to see how multi-level thinking can boost problem-solving accuracy.

Getting Started Tips

1. Try GPT-5 on ChatGPT or Copilot:

   • Visit openai.com or use Copilot in Microsoft products.
   • Type your question or upload a file—no technical skills needed.
   • Experiment with different prompts: “Summarize this,” “Explain step by step,” “Describe this image.”

2. Explore the API (for the curious):

   • An API is a way for apps to talk to GPT-5. If you’re not a developer, you can skip this for now.
   • If you want to learn more, check out beginner tutorials like OpenAI’s API Quickstart.

3. Use Long Contexts:

   • Paste long documents, articles, or code and ask for summaries or answers.
   • Example: “Summarize this contract in plain English.”

4. Ask for Explanations:

   • Use prompts like “Explain your reasoning” or “Show all steps” to learn as you go.
   • Example: “Help me solve this math problem step by step.”

5. Stay Safe and Smart:

   • Double-check important answers.
   • Use is it as a helpful assistant, not a replacement for professionals.

6. Find Tutorials and Help:

   • Explore beginner guides on OpenAI’s Help Center or Data Science Dojo’s blog.
   • Search YouTube for “GPT-5 beginner tutorial” for video walkthroughs.

Curious about AI models beyond GPT-5? Explore Grok-4, the XAI-powered model making waves in reasoning and real-time information retrieval.

Conclusion

GPT-5 marks a new era in artificial intelligence—combining smarter reasoning, massive memory, and seamless multimodal abilities into a single, user-friendly package. Whether you’re a curious beginner exploring AI for the first time or a seasoned developer building advanced applications, GPT-5 adapts to your needs. With its improved accuracy, powerful coding skills, and integration into everyday tools, GPT-5 isn’t just an upgrade—it’s a step toward AI that works alongside you like a true digital partner. Now is the perfect time to experiment, learn, and see firsthand how GPT-5 can transform the way you think, create, and work.

Ready to explore more?
Start your journey with Data Science Dojo’s Agentic AI Bootcamp and join the conversation on the future of open AI!

Graph rag is rapidly emerging as the gold standard for context-aware AI, transforming how large language models (LLMs) interact with knowledge. In this comprehensive guide, we’ll explore the technical foundations, architectures, use cases, and best practices of graph rag versus traditional RAG, helping you understand which approach is best for your enterprise AI, research, or product development needs.

Why Graph RAG Matters

Graph rag sits at the intersection of retrieval-augmented generation, knowledge graph engineering, and advanced context engineering. As organizations demand more accurate, explainable, and context-rich AI, graph rag is becoming essential for powering next-generation enterprise AI, agentic AI, and multi-hop reasoning systems.

Traditional RAG systems have revolutionized how LLMs access external knowledge, but they often fall short when queries require understanding relationships, context, or reasoning across multiple data points. Graph rag addresses these limitations by leveraging knowledge graphs—structured networks of entities and relationships—enabling LLMs to reason, traverse, and synthesize information in ways that mimic human cognition.

For organizations and professionals seeking to build robust, production-grade AI, understanding the nuances of graph rag is crucial. Data Science Dojo’s LLM Bootcamp and Agentic AI resources are excellent starting points for mastering these concepts.

What is Retrieval-Augmented Generation (RAG)?

Retrieval-augmented generation (RAG) is a foundational technique in modern AI, especially for LLMs. It bridges the gap between static model knowledge and dynamic, up-to-date information by retrieving relevant data from external sources at inference time.

How RAG Works

1. Indexing: Documents are chunked and embedded into a vector database.
2. Retrieval: At query time, the system finds the most semantically relevant chunks using vector similarity search.
3. Augmentation: Retrieved context is concatenated with the user’s prompt and fed to the LLM.
4. Generation: The LLM produces a grounded, context-aware response.

Benefits of RAG:

• Reduces hallucinations
• Enables up-to-date, domain-specific answers
• Provides source attribution
• Scales to enterprise knowledge needs

For a hands-on walkthrough, see RAG in LLM – Elevate Your Large Language Models Experience and What is Context Engineering?.

What is Graph RAG?

Graph rag is an advanced evolution of RAG that leverages knowledge graphs—structured representations of entities (nodes) and their relationships (edges). Instead of retrieving isolated text chunks, graph rag retrieves interconnected entities and their relationships, enabling multi-hop reasoning and deeper contextual understanding.

Key Features of Graph RAG

• Multi-hop Reasoning: Answers complex queries by traversing relationships across multiple entities.
• Contextual Depth: Retrieves not just facts, but the relationships and context connecting them.
• Structured Data Integration: Ideal for enterprise data, scientific research, and compliance scenarios.
• Explainability: Provides transparent reasoning paths, improving trust and auditability.

Learn more about advanced RAG techniques in the Large Language Models Bootcamp.

Technical Architecture: RAG vs Graph RAG

Traditional RAG Pipeline

• Vector Database: Stores embeddings of text chunks.
• Retriever: Finds top-k relevant chunks for a query using vector similarity.
• LLM: Generates a response using retrieved context.

Limitations:

Traditional RAG is limited to single-hop retrieval and struggles with queries that require understanding relationships or synthesizing information across multiple documents.

Graph RAG Pipeline

• Knowledge Graph: Stores entities and their relationships as nodes and edges.
• Graph Retriever: Traverses the graph to find relevant nodes, paths, and multi-hop connections.
• LLM: Synthesizes a response using both entities and their relationships, often providing reasoning chains.

Why Graph RAG Excels:

Graph rag enables LLMs to answer questions that require understanding of how concepts are connected, not just what is written in isolated paragraphs. For example, in healthcare, graph rag can connect symptoms, treatments, and patient history for more accurate recommendations.

For a technical deep dive, see Mastering LangChain and Retrieval Augmented Generation.

Key Differences and Comparative Analysis

Use Cases: When to Use RAG vs Graph RAG

Traditional RAG

• Customer support chatbots
• FAQ answering
• Document summarization
• News aggregation
• Simple enterprise search

Graph RAG

• Enterprise AI: Unified search across siloed databases, CRMs, and wikis.
• Healthcare: Multi-hop reasoning over patient data, treatments, and research.
• Finance: Compliance checks by tracing relationships between transactions and regulations.
• Scientific Research: Discovering connections between genes, diseases, and drugs.
• Personalization: Hyper-personalized recommendations by mapping user preferences to product graphs.

Explore more enterprise applications in Data and Analytics Services.

Case Studies: Real-World Impact

Case Study 1: Healthcare Knowledge Assistant

A leading hospital implemented graph rag to power its clinical decision support system. By integrating patient records, drug databases, and medical literature into a knowledge graph, the assistant could answer complex queries such as:

• “What is the recommended treatment for a diabetic patient with hypertension and a history of kidney disease?”

Impact:

• Reduced diagnostic errors by 30%
• Improved clinician trust due to transparent reasoning paths

Case Study 2: Financial Compliance

A global bank used graph rag to automate compliance checks. The system mapped transactions, regulations, and customer profiles in a knowledge graph, enabling multi-hop queries like:

• “Which transactions are indirectly linked to sanctioned entities through intermediaries?”

Impact:

• Detected 2x more suspicious patterns than traditional RAG
• Streamlined audit trails for regulatory reporting

Case Study 3: Data Science Dojo’s LLM Bootcamp

Participants in the LLM Bootcamp built both RAG and graph rag pipelines. They observed that graph rag consistently outperformed RAG in tasks requiring reasoning across multiple data sources, such as legal document analysis and scientific literature review.

Best Practices for Implementation

1. Start with RAG:

   Use traditional RAG for unstructured data and simple Q&A.

2. Adopt Graph RAG for Complexity:

   When queries require multi-hop reasoning or relationship mapping, transition to graph rag.

3. Leverage Hybrid Approaches:

   Combine vector search and graph traversal for maximum coverage.

4. Monitor and Benchmark:

   Use hybrid scorecards to track both AI quality and engineering velocity.

5. Iterate Relentlessly:

   Experiment with chunking, retrieval, and prompt formats for optimal results.

6. Treat Context as a Product:

   Apply version control, quality checks, and continuous improvement to your context pipelines.

7. Structure Prompts Clearly:

   Separate instructions, context, and queries for clarity.

8. Leverage In-Context Learning:

   Provide high-quality examples in the prompt.

9. Security and Compliance:

   Guard against prompt injection, data leakage, and unauthorized tool use.

10. Ethics and Privacy:

    Ensure responsible use of interconnected personal or proprietary data.

For more, see What is Context Engineering?

Challenges, Limitations, and Future Trends

Challenges

• Context Quality Paradox: More context isn’t always better—balance breadth and relevance.
• Scalability: Graph rag can be resource-intensive; optimize graph size and traversal algorithms.
• Security: Guard against data leakage and unauthorized access to sensitive relationships.
• Ethics and Privacy: Ensure responsible use of interconnected personal or proprietary data.
• Performance: Graph traversal can introduce latency compared to vector search.

Future Trends

• Context-as-a-Service: Platforms offering dynamic context assembly and delivery.
• Multimodal Context: Integrating text, audio, video, and structured data.
• Agentic AI: Embedding graph rag in multi-step agent loops with planning, tool use, and reflection.
• Automated Knowledge Graph Construction: Using LLMs and data pipelines to build and update knowledge graphs in real time.
• Explainable AI: Graph rag’s reasoning chains will drive transparency and trust in enterprise AI.

Emerging trends include context-as-a-service platforms, multimodal context (text, audio, video), and contextual AI ethics frameworks. For more, see Agentic AI.

Frequently Asked Questions (FAQ)

Q1: What is the main advantage of graph rag over traditional RAG?

A: Graph rag enables multi-hop reasoning and richer, more accurate responses by leveraging relationships between entities, not just isolated facts.

Q2: When should I use graph rag?

A: Use graph rag when your queries require understanding of how concepts are connected—such as in enterprise search, compliance, or scientific discovery.

Q3: What frameworks support graph rag?

A: Popular frameworks include LangChain and LlamaIndex, which offer orchestration, memory management, and integration with vector databases and knowledge graphs.

Q4: How do I get started with RAG and graph rag?

A: Begin with Retrieval Augmented Generation and explore advanced techniques in the LLM Bootcamp.

Q5: Is graph rag slower than traditional RAG?

A: Graph rag can be slower due to graph traversal and reasoning, but it delivers superior accuracy and explainability for complex queries 1.

Q6: Can I combine RAG and graph rag in one system?

A: Yes! Many advanced systems use a hybrid approach, first retrieving relevant documents with RAG, then mapping entities and relationships with graph rag for deeper reasoning.

Conclusion & Next Steps

Graph rag is redefining what’s possible with retrieval-augmented generation. By enabling LLMs to reason over knowledge graphs, organizations can unlock new levels of accuracy, transparency, and insight in their AI systems. Whether you’re building enterprise AI, scientific discovery tools, or next-gen chatbots, understanding the difference between graph rag and traditional RAG is essential for staying ahead.

Ready to build smarter AI?

• Explore Data Science Dojo’s LLM Bootcamp for hands-on training.
• Dive into Mastering LangChain for practical graph rag workflows.
• Stay updated with the latest in Agentic AI.

GPT OSS is OpenAI’s latest leap in democratizing artificial intelligence, offering open-weight large language models (LLMs) that anyone can download, run, and fine-tune on their own hardware. Unlike proprietary models locked behind APIs, gpt oss models—gpt-oss-120b and gpt-oss-20b—are designed for transparency, customization, and local inference, marking a pivotal shift in the AI landscape.

Why GPT OSS Matters

The release of gpt oss signals a new era for open-weight models. For the first time since GPT-2, OpenAI has made the internal weights of its models publicly available under the Apache 2.0 license. This means developers, researchers, and enterprises can:

• Run models locally for privacy and low-latency applications.
• Fine-tune models for domain-specific tasks.
• Audit and understand model behavior for AI safety and compliance.

Key Features of GPT OSS

1. Open-Weight Models

GPT OSS models are open-weight, meaning their parameters are freely accessible. This transparency fosters innovation and trust, allowing the community to inspect, modify, and improve the models.

2. Large Language Model Architecture

Both gpt-oss-120b and gpt-oss-20b are built on advanced transformer architecture, leveraging mixture-of-experts (MoE) layers for efficient computation. The 120b model activates 5.1 billion parameters per token, while the 20b model uses 3.6 billion, enabling high performance with manageable hardware requirements.

3. Chain-of-Thought Reasoning

A standout feature of gpt oss is its support for chain-of-thought reasoning. This allows the models to break down complex problems into logical steps, improving accuracy in tasks like coding, math, and agentic workflows.

Want to explore context engineering? Check out this guide!

4. Flexible Deployment

With support for local inference, gpt oss can run on consumer hardware (16GB RAM for 20b, 80GB for 120b) or be deployed via cloud partners like Hugging Face, Azure, and more. This flexibility empowers organizations to choose the best fit for their needs.

5. Apache 2.0 License

The Apache 2.0 license grants broad rights to use, modify, and distribute gpt oss models—even for commercial purposes. This open licensing is a game-changer for startups and enterprises seeking to build proprietary solutions on top of state-of-the-art AI.

Technical Deep Dive: How GPT OSS Works

Transformer and Mixture-of-Experts

GPT OSS models use a transformer backbone with MoE layers, alternating dense and sparse attention for efficiency. Rotary Positional Embedding (RoPE) enables context windows up to 128,000 tokens, supporting long-form reasoning and document analysis.

Dive deep into what goes on in Mixture of Experts!

Fine-Tuning and Customization

Both models are designed for easy fine-tuning, enabling adaptation to specialized datasets or unique business needs. The open-weight nature means you can experiment with new training techniques, safety filters, or domain-specific optimizations.

Discover the Hidden Mechanics behind LLMs!

Tool Use and Agentic Tasks

GPT OSS excels at agentic tasks—using tools, browsing the web, executing code, and following complex instructions. This makes it ideal for building AI agents that automate workflows or assist with research.

10 Open Source Tools for Agentic AI that can make your life easy!

Benchmark Performance of GPT OSS: How Does It Stack Up?

GPT OSS models—gpt-oss-120b and gpt-oss-20b—were evaluated on a suite of academic and real-world tasks, here;s how they did:

gpt-oss-120b:

• Achieves near-parity with OpenAI’s o4-mini on core reasoning benchmarks.
• Outperforms o3-mini and matches or exceeds o4-mini on competition coding (Codeforces), general problem solving (MMLU, HLE), and tool calling (TauBench).
• Surpasses o4-mini on health-related queries (HealthBench) and competition mathematics (AIME 2024 & 2025).
• Delivers strong performance on few-shot function calling and agentic tasks, making it suitable for advanced AI agent development.

gpt-oss-20b:

• Matches or exceeds o3-mini on the same benchmarks, despite its smaller size.
• Outperforms o3-mini on competition mathematics and health-related tasks.
• Designed for efficient deployment on edge devices, offering high performance with just 16GB of memory.

Use Cases for GPT OSS

• Enterprise AI Agents:

  Build secure, on-premises AI assistants for sensitive data.

• Research and Education:

  Study model internals, experiment with new architectures, or teach advanced AI concepts.

• Healthcare and Legal:

  Fine-tune models for compliance-heavy domains where data privacy is paramount.

• Developer Tools:

  Integrate gpt oss into IDEs, chatbots, or automation pipelines.

Want to explore vibe coding? Check out this guide

Safety and Alignment in GPT OSS

OpenAI has prioritized AI safety in gpt oss, employing deliberative alignment and instruction hierarchy to minimize misuse. The models have undergone adversarial fine-tuning to test worst-case scenarios, with results indicating robust safeguards against harmful outputs.

A $500,000 red-teaming challenge encourages the community to identify and report vulnerabilities, further strengthening the safety ecosystem.

Discover the 5 core principles of Responsible AI

Getting Started with GPT OSS

Download and Run

• Hugging Face:

  Download model weights for local or cloud deployment.

• Ollama/LM Studio:

  Run gpt oss on consumer hardware with user-friendly interfaces.

• PyTorch/vLLM:

  Integrate with popular ML frameworks for custom workflows.

Fine-Tuning

Use your own datasets to fine-tune gpt oss for domain-specific tasks, leveraging the open architecture for maximum flexibility.

Community and Support

Join forums, contribute to GitHub repositories, and participate in safety challenges to shape the future of open AI.

Forget RAG, Agentic RAG can make your pipelines even better. Learn more in our guide

Frequently Asked Questions (FAQ)

Q1: What is the difference between gpt oss and proprietary models like GPT-4?

A: GPT OSS is open-weight, allowing anyone to download, inspect, and fine-tune the model, while proprietary models are only accessible via API and cannot be modified.

Q2: Can I use gpt oss for commercial projects?

A: Yes, the Apache 2.0 license permits commercial use, modification, and redistribution.

Q3: What hardware do I need to run gpt oss?

A: gpt-oss-20b runs on consumer hardware with 16GB RAM; gpt-oss-120b requires 80GB, typically a high-end GPU.

Q4: How does gpt oss handle safety and misuse?

A: OpenAI has implemented advanced alignment techniques and encourages community red-teaming to identify and mitigate risks.

Q5: Where can I learn more about deploying and fine-tuning gpt oss?

A: Check out LLM Bootcamp by Data Science Dojo and OpenAI’s official documentation.

Conclusion: The Future of Open AI with GPT OSS

GPT OSS is more than just a set of models—it’s a movement towards open, transparent, and customizable AI. By empowering developers and organizations to run, fine-tune, and audit large language models, gpt oss paves the way for safer, more innovative, and democratized artificial intelligence.

Ready to explore more?
Start your journey with Data Science Dojo’s Agentic AI Bootcamp and join the conversation on the future of open AI!

Welcome to the ultimate guide on google ai mode, the revolutionary way to search smarter, faster, and more intuitively. As artificial intelligence continues to reshape how we interact with technology, Google’s AI-powered search features are leading the charge. Whether you’re a data scientist, developer, or everyday user, understanding and leveraging google ai mode can dramatically enhance your productivity and search accuracy.

In this tutorial, we’ll break down what google ai mode is, how it works, and how you can use it to unlock advanced search capabilities. We’ll also explore the underlying AI technologies, practical use cases, and expert tips to help you stay ahead in the evolving world of search.

What Is Google AI Mode?

Google AI Mode refers to the integration of advanced artificial intelligence and machine learning algorithms into Google’s search platform. This mode leverages generative AI, natural language processing, and large language models (LLMs) to deliver more relevant, context-aware, and personalized search results.

Unlike traditional keyword-based search, google ai mode understands user intent, context, and even conversational queries. It can summarize information, answer complex questions, and suggest follow-up actions—making it a game-changer for anyone seeking deeper insights or faster answers.

How Does Google AI Mode Search Work?

At its core, google ai mode search uses a combination of:

• Natural Language Processing (NLP):

  Interprets user queries in plain language, understanding context and intent.

• Machine Learning Search Algorithms:

  Continuously learn from user interactions to improve result relevance.

• Generative AI:

  Summarizes content, generates answers, and even creates new information based on existing data.

• AI Agents:

  Act as intelligent assistants, guiding users through complex tasks or multi-step searches.

This approach enables google ai mode to move beyond simple keyword matching, offering a more conversational and interactive search experience.

Learn more about AI agents and agentic workflows.

Setting Up and Accessing Google AI Mode

Step 1: Ensure You Have Access

Google AI Mode is being rolled out across various products, including Google Search, Gemini, and Workspace tools. To access the latest AI-powered search features:

• Use the latest version of Google Chrome or the Google app.
• Sign in with your Google account.
• Opt into experimental features (such as Search Generative Experience, SGE) if available in your region.

Step 2: Activate AI Mode

• On desktop: Look for the “AI Mode” or “Labs” toggle in Google Search.
• On mobile: Tap the AI icon or “Try AI-powered search” prompt.
• For Gemini users: AI mode is often enabled by default.

Tip: If you don’t see these options, check Google’s official AI search updates or join the Search Labs program.

Key Features of Google AI Mode Search

a. Conversational Search

Ask questions in natural language and receive context-aware answers. Google AI Mode can handle follow-up questions, clarifications, and multi-turn conversations.

b. AI-Powered Summaries

Get concise, AI-generated summaries of complex topics, articles, or news stories—saving you time and effort.

c. Contextual Suggestions

Receive intelligent suggestions for related searches, actions, or next steps based on your query and browsing history.

d. Multimodal Search

Search using text, images, or even voice commands. Google AI Mode can interpret and combine multiple data types for richer results.

e. Personalized Results

AI algorithms tailor results based on your preferences, location, and past interactions, making search more relevant than ever.

Explore more about multimodality in LLMs.

Advanced Search Techniques with AI Mode

a. Using Natural Language Queries

Instead of typing keywords, try full questions or statements:

• “Show me the latest trends in generative AI for business.”
• “Summarize the key findings from the latest AI research papers.”

b. Leveraging AI Agents

Ask Google to perform tasks:

• “Compare Python and R for machine learning.”
• “Find tutorials on building AI-powered chatbots.”

c. Combining Search Modes

Use images, voice, or text together:

• Upload a chart and ask, “What does this data suggest about sales trends?”
• Speak a question and follow up with a typed clarification.

d. Exploring Generative AI Features

• Request summaries, code snippets, or even creative content.
• Use AI-powered tools to draft emails, generate reports, or brainstorm ideas.

For hands-on learning, check out Data Science Dojo’s LLM Bootcamp.

Real-World Use Cases and Applications

a. Research and Academia

• Quickly summarize academic papers or extract key insights.
• Find related research using semantic search.

b. Business Intelligence

• Analyze market trends with AI-generated reports.
• Automate competitor analysis and data gathering.

c. Coding and Development

• Get code suggestions, debug errors, or generate documentation.

d. Everyday Productivity

• Plan trips, manage schedules, or draft content using AI-powered search.
• Integrate with Google Workspace for seamless workflow optimization.

Learn more about AI code generation tools.

Optimizing Your Workflow with Google AI Mode

a. Personalize Your Experience

• Adjust search settings to prioritize certain types of results.
• Use Google’s activity controls to refine AI recommendations.

b. Integrate with Other Tools

• Connect Google AI Mode with productivity apps, calendars, or note-taking tools.
• Use APIs for custom AI-powered search solutions.

c. Stay Updated

• Follow Data Science Dojo’s blog for the latest AI search trends and tutorials.
• Join Google’s beta programs to access new features early.

Troubleshooting and Best Practices

• Accuracy: While google ai mode is powerful, always verify critical information, especially for business or academic use.
• Privacy: Review Google’s privacy settings to control data usage.
• Limitations: Some features may be region-specific or require enrollment in experimental programs.

For more on responsible AI, read What is Responsible AI?

How Google AI Mode Improves Search: A Real Example

To truly appreciate the power of Google AI Mode, let’s look at how it enhances traditional search with an example.

Scenario: You’re researching how to implement an LLM-powered chatbot.

Traditional Search (Without AI Mode)

Query: build chatbot with LLM

Typical Result:

• A list of blog posts and articles

• StackOverflow threads

• YouTube videos

• Some GitHub repos

User Experience:
You’ll need to click through multiple links, skim or read in full, and piece together a mental map of the steps, tools, and best practices.

AI Mode Search

Query (Natural Language):
“How can I build a chatbot using a large language model like GPT?”

AI-Powered Result:

• A step-by-step AI-generated summary of the development process (e.g., choose an LLM, set up environment, define prompts, deploy on an app).

• Suggested tools: LangChain, OpenAI API, Flask.

• A code snippet for a basic chatbot using Python and OpenAI’s API.

• Follow-up prompts like:

  • “What are the best frameworks for building chatbots?”

  • “How do I make my chatbot more conversational?”

• Option to expand the summary into detailed documentation or tutorials.

Frequently Asked Questions (FAQ)

Q1: What is google ai mode?

A: Google AI Mode is an advanced search feature that uses artificial intelligence and machine learning to deliver more relevant, context-aware, and conversational search results.

Q2: How do I enable google ai mode?

A: Look for the AI Mode or Labs toggle in Google Search, or join the Search Labs program for early access.

Q3: Can I use google ai mode for coding help?

A: Yes! Google AI Mode can generate code snippets, debug errors, and suggest best practices. Learn more about AI code generation.

Q4: Is my data safe with google ai mode?

A: Google provides privacy controls and transparency reports. Always review your settings and understand how your data is used.

Q5: Where can I learn more about AI-powered search?

A: Explore Data Science Dojo’s tutorials and AI blog category.

Conclusion & Next Steps

Google AI Mode is transforming how we search, learn, and work. By harnessing the power of AI-powered search, you can unlock deeper insights, save time, and stay ahead in the data-driven world. Whether you’re a beginner or an expert, mastering google ai mode will give you a competitive edge.

Ready to take your skills further?

• Join Data Science Dojo’s LLM Bootcamp for hands-on training.
• Subscribe to the Data Science Dojo newsletter for the latest AI updates.
• Explore more AI tutorials and resources.

Start experimenting with google ai mode today and share your experiences in the Data Science Dojo community forum. Have questions or want to learn more? Contact our team for personalized guidance!

The hierarchical reasoning model is revolutionizing how artificial intelligence (AI) systems approach complex problem-solving. At the very beginning of this post, let’s clarify: the hierarchical reasoning model is a brain-inspired architecture that enables AI to break down and solve intricate tasks by leveraging multi-level reasoning, adaptive computation, and deep latent processing. This approach is rapidly gaining traction in the data science and machine learning communities, promising a leap toward true artificial general intelligence.

What is a Hierarchical Reasoning Model?

A hierarchical reasoning model (HRM) is an advanced AI architecture designed to mimic the brain’s ability to process information at multiple levels of abstraction and timescales. Unlike traditional deep learning architectures, which often rely on fixed-depth layers, HRMs employ a nested, recurrent structure. This allows them to perform multi-level reasoning—from high-level planning to low-level execution—within a single, unified model.

Master the building blocks of modern AI with hands-on deep learning tutorials and foundational concepts.

Why Standard AI Models Hit a Ceiling

Most large language models (LLMs) and deep learning systems use a fixed number of layers. Whether solving a simple math problem or navigating a complex maze, the data passes through the same computational depth. This limitation, known as fixed computational depth, restricts the model’s ability to handle tasks that require extended, step-by-step reasoning.

Chain-of-thought prompting has been a workaround, where models are guided to break down problems into intermediate steps. However, this approach is brittle, data-hungry, and often slow, especially for tasks demanding deep logical inference or symbolic manipulation.

The Brain-Inspired Solution: Hierarchical Reasoning Model Explained

The hierarchical reasoning model draws inspiration from the human brain’s hierarchical and multi-timescale processing. In the brain, higher-order regions handle abstract planning over longer timescales, while lower-level circuits execute rapid, detailed computations. HRM replicates this by integrating two interdependent recurrent modules:

High-Level Module: Responsible for slow, abstract planning and global strategy.
Low-Level Module: Handles fast, detailed computations and local problem-solving.

This nested loop allows the model to achieve significant computational depth and flexibility, overcoming the limitations of fixed-layer architectures.

Uncover the next generation of AI reasoning with Algorithm of Thoughts and its impact on complex problem-solving.

Technical Architecture: How Hierarchical Reasoning Model Works

1. Latent Reasoning and Fixed-Point Convergence

Latent reasoning in HRM refers to the model’s ability to perform complex, multi-step computations entirely within its internal neural states—without externalizing intermediate steps as text, as is done in chain-of-thought (CoT) prompting. This is a fundamental shift: while CoT models “think out loud” by generating step-by-step text, HRM “thinks silently,” iterating internally until it converges on a solution.

How HRM Achieves Latent Reasoning

• Hierarchical Modules: HRM consists of two interdependent recurrent modules:
  • A high-level module (H) for slow, abstract planning.
  • A low-level module (L) for rapid, detailed computation.
• Nested Iteration: For each high-level step, the low-level module performs multiple fast iterations, refining its state based on the current high-level context.
• Hierarchical Convergence: The low-level module converges to a local equilibrium (fixed point) within each high-level cycle. After several such cycles, the high-level module itself converges to a global fixed point representing the solution.
• Fixed-Point Solution: The process continues until both modules reach a stable state—this is the “fixed point.” The final output is generated from this converged high-level state.

Analogy:

Imagine a manager (high-level) assigning a task to an intern (low-level). The intern works intensely, reports back, and the manager updates the plan. This loop continues until both agree the task is complete. All this “reasoning” happens internally, not as a written log.

Learn how context engineering is redefining reliability and performance in advanced AI and RAG systems.

Why is this powerful?

• It allows the model to perform arbitrarily deep reasoning in a single forward pass, breaking free from the fixed-depth limitation of standard Transformers.
• It enables the model to “think” as long as needed for each problem, rather than being constrained by a fixed number of layers or steps.

2. Efficient Training with the Implicit Function Theorem

Training deep, recurrent models like Hierarchical Reasoning Model is challenging because traditional backpropagation through time (BPTT) requires storing all intermediate states, leading to high memory and computational costs.

HRM’s Solution: The Implicit Function Theorem (IFT)

• Fixed-Point Gradients: If a recurrent network converges to a fixed point, the gradient of the loss with respect to the model parameters can be computed directly at that fixed point, without unrolling all intermediate steps.
• 1-Step Gradient Approximation: In practice, HRM uses a “1-step gradient” approximation, replacing the matrix inverse with the identity matrix for efficiency.
• This allows gradients to be computed using only the final states, drastically reducing memory usage (from O(T) to O(1), where T is the number of steps).

Benefits:

• Scalability: Enables training of very deep or recurrent models without running out of memory.
• Biological Plausibility: Mirrors how the brain might perform credit assignment without replaying all past activity.
• Practicality: Works well in practice for equilibrium models like HRM, as shown in recent research.

3. Adaptive Computation with Q-Learning

Not all problems require the same amount of reasoning. HRM incorporates an adaptive computation mechanism to dynamically allocate more computational resources to harder problems and stop early on easier ones.

How Adaptive Computation Works in HRM

• Q-Head: Hierarchical Reasoning Model includes a Q-learning “head” that predicts the value of two actions at each reasoning segment: “halt” or “continue.”
• Decision Process:
  • After each segment (a set of reasoning cycles), the Q-head evaluates whether to halt (output the current solution) or continue reasoning.
  • The decision is based on the predicted Q-values and a minimum/maximum segment threshold.
• Reinforcement Learning: The Q-head is trained using Q-learning, where:
  • Halting yields a reward if the prediction is correct.
  • Continuing yields no immediate reward but allows further refinement.
• Stability: HRM achieves stable Q-learning without the usual tricks (like replay buffers) by using architectural features such as RMSNorm and AdamW, which keep weights bounded.

Benefits:

• Efficiency: The model learns to “think fast” on easy problems and “think slow” (i.e., reason longer) on hard ones, mirroring human cognition.
• Resource Allocation: Computational resources are used where they matter most, improving both speed and accuracy.

Key Advantages Over Chain-of-Thought and Transformers

1. Greater Computational Depth: Hierarchical Reasoning Model can perform arbitrarily deep reasoning within a single forward pass, unlike fixed-depth Transformers.
2. Data Efficiency: Achieves high performance on complex tasks with fewer training samples.
3. Biological Plausibility: Mimics the brain’s hierarchical organization, leading to emergent properties like dimensionality hierarchy.
4. Scalability: Efficient memory usage and training stability, even for long reasoning chains.

Demystify large language models and uncover the secrets powering conversational AI like ChatGPT.

Real-World Applications

The hierarchical reasoning model has demonstrated exceptional results in:

1. Solving complex Sudoku puzzles and symbolic logic tasks
2. Optimal pathfinding in large mazes
3. Abstraction and Reasoning Corpus (ARC) benchmarks—a key test for artificial general intelligence
4. General-purpose planning and decision-making in agentic AI systems

These applications highlight HRM’s potential to power next-generation AI systems capable of robust, flexible, and generalizable reasoning.

Challenges and Future Directions

While the hierarchical reasoning model is a breakthrough, several challenges remain:

Interpretability:

Understanding the internal reasoning strategies of HRMs is still an open research area.

Integration with memory and attention:

Future models may combine HRM with hierarchical memory systems for even greater capability.

Broader adoption:

As HRM matures, expect to see its principles integrated into mainstream AI frameworks and libraries.

Empower your AI projects with the best open-source tools for building agentic and autonomous systems.

Frequently Asked Questions (FAQ)

Q1: What makes the hierarchical reasoning model different from standard neural networks?

A: HRM uses a nested, recurrent structure that allows for multi-level, adaptive reasoning, unlike standard fixed-depth networks.

Q2: How does Hierarchical Reasoning Model achieve better performance on complex reasoning tasks?

A: By leveraging hierarchical modules and latent reasoning, HRM can perform deep, iterative computations efficiently.

Q3: Is HRM biologically plausible?

A: Yes, HRM’s architecture is inspired by the brain’s hierarchical processing and has shown emergent properties similar to those observed in neuroscience.

Q4: Where can I learn more about HRM?

A: Check out the arXiv paper on Hierarchical Reasoning Model by Sapient Intelligence and Data Science Dojo’s blog on advanced AI architectures.

Conclusion & Next Steps

The hierarchical reasoning model represents a paradigm shift in AI, moving beyond shallow, fixed-depth architectures to embrace the power of hierarchy, recurrence, and adaptive computation. As research progresses, expect HRM to play a central role in the development of truly intelligent, general-purpose AI systems.

Ready to dive deeper?
Explore more on Data Science Dojo’s blog for tutorials, case studies, and the latest in AI research.

Replit is transforming how developers, data scientists, and educators code, collaborate, and innovate. Whether you’re building your first Python script, prototyping a machine learning model, or teaching a classroom of future programmers, Replit’s cloud-based IDE and collaborative features are redefining what’s possible in modern software development.

What’s more, Replit is at the forefront of agentic coding—enabling AI-powered agents to assist with end-to-end development tasks like code generation, debugging, refactoring, and context-aware recommendations. These intelligent coding agents elevate productivity, reduce cognitive load, and bring a new level of autonomy to the development process.

In this comprehensive guide, we’ll explore what makes Replit a game-changer for the data science and technology community, how it empowers rapid prototyping, collaborative and agentic coding, and why it’s the go-to platform for both beginners and professionals.

What is Replit?

Replit is a cloud-based integrated development environment (IDE) that allows users to write, run, and share code directly from their browser. Supporting dozens of programming languages—including Python, JavaScript, Java, and more—Replit eliminates the need for complex local setups, making coding accessible from any device, anywhere.

At its core, Replit is about collaborative coding, rapid prototyping, and increasingly, agentic coding. With the integration of AI-powered features like Ghostwriter, Replit enables developers to go beyond autocomplete—supporting autonomous agents that can understand project context, generate multi-step code, refactor intelligently, and even debug proactively. This shift toward agentic workflows allows individuals, teams, classrooms, and open-source communities to build, test, and deploy software not just quickly, but with intelligent assistance that evolves alongside the codebase.

For more on vibe coding and AI-driven development, check out The Ultimate Guide to Vibe Coding

Why Replit Matters for Data Science and Technology

The rise of cloud IDEs is reshaping the landscape of software development and data science. Here’s why:

• Accessibility:

  No installation required—just open your browser and start coding.

• Collaboration:

  Real-time code sharing and editing, perfect for remote teams and classrooms.

• Rapid Prototyping:

  Instantly test ideas, build MVPs, and iterate without friction.

• Education:

  Lower the barrier to entry for new programmers and data scientists.

• Integration:

  Seamlessly connect with GitHub, APIs, and data science libraries.

From Python to projects—learn the real-world skills and tools that power today’s most successful data scientists.

For data scientists, it offers a Python online environment with built-in support for popular libraries, making it ideal for experimenting with machine learning, data analysis, and visualization.

Key Features of Replit

1. Cloud IDE

Replit’s cloud IDE supports over 50 programming languages. Its intuitive interface includes a code editor, terminal, and output console—all in your browser. You can run code, debug, and visualize results without any local setup.

2. Collaborative Coding

Invite teammates or students to your “repl” (project) and code together in real time. See each other’s cursors, chat, and build collaboratively—no more emailing code files or dealing with version conflicts.

3. Instant Hosting & Deployment

Deploy web apps, APIs, and bots with a single click. Replit provides instant hosting, making it easy to share your projects with the world.

4. AI Coding Assistant: Ghostwriter

Replit’s Ghostwriter is an AI-powered coding assistant that helps you write, complete, and debug code. It understands context, suggests improvements, and accelerates development—especially useful for data science workflows and rapid prototyping.

5. Templates & Community Projects

Start from scratch or use community-contributed templates for web apps, data science notebooks, games, and more. Explore, fork, and remix projects to learn and innovate.

6. Education Tools

Replit for Education offers classroom management, assignments, and grading tools, making it a favorite among teachers and students.

Unlock the creative power of generative AI with the most essential Python libraries—your toolkit for building intelligent, adaptive systems.

Getting Started: Your First Project

1. Sign Up:

   Create a free account at replit.com.

2. Create a Repl:

   Choose your language (e.g., Python, JavaScript) and start a new project.

3. Write Code:

   Use the editor to write your script or application.

4. Run & Debug:

   Click “Run” to execute your code. Use the built-in debugger for troubleshooting.

5. Share:

   Invite collaborators or share a public link to your project.

Tip: For data science, select the Python template and install libraries like pandas, numpy, or matplotlib using the built-in package manager.

Collaborative Coding: Real-Time Teamwork in the Cloud

Replit’s collaborative features are a game-changer for remote teams, hackathons, and classrooms:

• Live Editing:

  Multiple users can edit the same file simultaneously.

• Chat & Comments:

  Communicate directly within the IDE.

• Version Control:

  Track changes, revert to previous versions, and manage branches.

• Code Sharing:

  Share your project with a link—no downloads required.

This makes Replit ideal for pair programming, code reviews, and group projects.

Replit Ghostwriter: AI Coding Assistant for Productivity

Ghostwriter is Replit’s built-in AI coding assistant, designed to boost productivity and learning:

• Code Completion:

  Suggests code as you type, reducing syntax errors.

• Bug Detection:

  Highlights potential issues and suggests fixes.

• Documentation:

  Explains code snippets and APIs in plain language.

• Learning Aid:

  Great for beginners learning new languages or frameworks.

Ghostwriter leverages the latest advances in AI and large language models, similar to tools like GitHub Copilot, but fully integrated into the Replit ecosystem.

Understand how the Model Context Protocol (MCP) bridges LLMs to real-world tools, enabling truly agentic behavior.

Replit for Education: Empowering the Next Generation

Replit is revolutionizing education technology by making coding accessible and engaging:

• Classroom Management:

  Teachers can create assignments, monitor progress, and provide feedback.

• No Setup Required:

  Students can code from Chromebooks, tablets, or any device.

• Interactive Learning:

  Real-time collaboration and instant feedback foster active learning.

• Community Support:

  Access to tutorials, challenges, and a global network of learners.

Educators worldwide use Replit to teach Python, web development, data science, and more.

Integrating Replit with Data Science Workflows

For data scientists and analysts, Replit offers:

• Python Online:

  Run Jupyter-like notebooks, analyze data, and visualize results.

• Library Support:

  Install and use libraries like pandas, scikit-learn, TensorFlow, and matplotlib.

• API Integration:

  Connect to external data sources, APIs, and databases.

• Rapid Prototyping:

  Test machine learning models and data pipelines without local setup.

Discover how context engineering shapes smarter AI agents—by teaching models to think beyond the next token.

Example: Build a machine learning model in Python, visualize results with matplotlib, and share your findings—all within Replit.

Open-Source, Community, and Vibe Coding

Replit is at the forefront of the vibe coding movement—using natural language and AI to turn ideas into code. Its open-source ethos and active community mean you can:

• Fork & Remix: Explore thousands of public projects and build on others’ work.
• Contribute: Share your own templates, libraries, or tutorials.
• Learn Prompt Engineering: Experiment with AI-powered coding assistants and prompt-based development.

Explore how open-source tools are powering the rise of agentic AI—where code doesn’t just respond, it acts.

Limitations and Best Practices

While Replit is powerful, it’s important to be aware of its limitations:

• Resource Constraints: Free accounts have limited CPU, memory, and storage.
• Data Privacy: Projects are public by default unless you upgrade to a paid plan.
• Package Support: Some advanced libraries or system-level dependencies may not be available.
• Performance: For large-scale data processing, local or cloud VMs may be more suitable.

Best Practices:

• Use Replit for prototyping, learning, and collaboration.
• For production workloads, consider exporting your code to a local or cloud environment.
• Always back up important projects.

Frequently Asked Questions (FAQ)

Q1: Is Replit free to use?

Yes, Replit offers a generous free tier. Paid plans unlock private projects, more resources, and advanced features.

Q2: Can I use Replit for data science?

Absolutely! Replit supports Python and popular data science libraries, making it ideal for analysis, visualization, and machine learning.

Q3: How does Replit compare to Jupyter Notebooks?

Replit offers a browser-based coding environment with real-time collaboration, instant hosting, and support for multiple languages. While Jupyter is great for notebooks, Replit excels in collaborative, multi-language projects.

Q4: What is Ghostwriter?

Ghostwriter is Replit’s AI coding assistant, providing code completion, bug detection, and documentation support.

Q5: Can I deploy web apps on Replit?

Yes, you can deploy web apps, APIs, and bots with a single click and share them instantly.

Conclusion & Next Steps

Replit is more than just a cloud IDE—it’s a platform for collaborative coding, rapid prototyping, and AI-powered development. Whether you’re a data scientist, educator, or developer, this AI powered cloud IDE empowers you to build, learn, and innovate without barriers.

Ready to experience the future of coding?

• Sign up at replit.com and start your first project.
• Explore Data Science Dojo’s blog for more tutorials on cloud IDEs, AI coding assistants, and data science workflows.
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Small language models are rapidly transforming the landscape of artificial intelligence, offering a powerful alternative to their larger, resource-intensive counterparts. As organizations seek scalable, cost-effective, and privacy-conscious AI solutions, small language models are emerging as the go-to choice for a wide range of applications.

In this blog, we’ll explore what small language models are, how they work, their advantages and limitations, and why they’re poised to shape the next wave of AI innovation.

What Are Small Language Models?

Small language models (SLMs) are artificial intelligence models designed to process, understand, and generate human language, but with a much smaller architecture and fewer parameters than large language models (LLMs) like GPT-4 or Gemini. Typically, SLMs have millions to a few billion parameters, compared to LLMs, which can have hundreds of billions or even trillions. This compact size makes SLMs more efficient, faster to train, and easier to deploy—especially in resource-constrained environments such as edge devices, mobile apps, or scenarios requiring on-device AI and offline inference.

Understand Transformer models as the future of Natural Language Processing

How Small Language Models Function

Core Architecture

Small language models are typically built on the same foundational architecture as LLMs: the Transformer. The Transformer architecture uses self-attention mechanisms to process input sequences in parallel, enabling efficient handling of language tasks. However, SLMs are designed to be lightweight, with parameter counts ranging from a few million to a few billion—far less than the hundreds of billions or trillions in LLMs. This reduction is achieved through several specialized techniques:

Key Techniques Used in SLMs

1. Model Compression

   • Pruning: Removes less significant weights or neurons from the model, reducing size and computational requirements while maintaining performance.
   • Quantization: Converts high-precision weights (e.g., 32-bit floats) to lower-precision formats (e.g., 8-bit integers), decreasing memory usage and speeding up inference.
   • Structured Pruning: Removes entire groups of parameters (like neurons or layers), making the model more hardware-friendly.

2. Knowledge Distillation

   • A smaller “student” model is trained to replicate the outputs of a larger “teacher” model. This process transfers knowledge, allowing the SLM to achieve high performance with fewer parameters.
   • Learn more in this detailed guide on knowledge distillation

3. Efficient Self-Attention Approximations

   • SLMs often use approximations or optimizations of the self-attention mechanism to reduce computational complexity, such as sparse attention or linear attention techniques.

4. Parameter-Efficient Fine-Tuning (PEFT)

   • Instead of updating all model parameters during fine-tuning, only a small subset or additional lightweight modules are trained, making adaptation to new tasks more efficient.

5. Neural Architecture Search (NAS)

   • Automated methods are used to discover the most efficient model architectures tailored for specific tasks and hardware constraints.

6. Mixed Precision Training

   • Uses lower-precision arithmetic during training to reduce memory and computational requirements without sacrificing accuracy.

7. Data Augmentation

   • Expands the training dataset with synthetic or varied examples, improving generalization and robustness, especially when data is limited.

For a deeper dive into these techniques, check out Data Science Dojo’s guide on model compression and optimization.

How SLMs Differ from LLMs

Structure

• SLMs: Fewer parameters (millions to a few billion), optimized for efficiency, often use compressed or distilled architectures.
• LLMs: Massive parameter counts (tens to hundreds of billions), designed for general-purpose language understanding and generation.

Performance

• SLMs: Excel at domain-specific or targeted tasks, offer fast inference, and can be fine-tuned quickly. May struggle with highly complex or open-ended tasks that require broad world knowledge.
• LLMs: Superior at complex reasoning, creativity, and generalization across diverse topics, but require significant computational resources and have higher latency.

Deployment

• SLMs: Can run on CPUs, edge devices, mobile phones, and in offline environments. Ideal for on-device AI, privacy-sensitive applications, and scenarios with limited hardware.
• LLMs: Typically require powerful GPUs or cloud infrastructure.

Advantages of Small Language Models

1. Efficiency and Speed

SLMs require less computational power, making them ideal for edge AI and on-device AI scenarios. They enable real-time inference and can operate offline, which is crucial for applications in healthcare, manufacturing, and IoT.

2. Cost-Effectiveness

Training and deploying small language models is significantly less expensive than LLMs. This democratizes AI, allowing startups and smaller organizations to leverage advanced NLP without breaking the bank.

3. Privacy and Security

SLMs can be deployed on-premises or on local devices, ensuring sensitive data never leaves the organization. This is a major advantage for industries with strict privacy requirements, such as finance and healthcare.

4. Customization and Domain Adaptation

Fine-tuning small language models on proprietary or domain-specific data leads to higher accuracy and relevance for specialized tasks, reducing the risk of hallucinations and irrelevant outputs.

5. Sustainability

With lower energy consumption and reduced hardware needs, SLMs contribute to more environmentally sustainable AI solutions.

Limitations of Small Language Models

While small language models offer many benefits, they also come with trade-offs:

• Limited Generalization: SLMs may struggle with open-ended or highly complex tasks that require broad world knowledge.
• Performance Ceiling: For tasks demanding deep reasoning or creativity, LLMs still have the edge.
• Maintenance Complexity: Organizations may need to manage multiple SLMs for different domains, increasing integration complexity.

Real-World Use Cases for Small Language Models

Small language models are already powering a variety of applications across industries:

• Chatbots and Virtual Assistants: Fast, domain-specific customer support with low latency.
• Content Moderation: Real-time filtering of user-generated content on social platforms.
• Sentiment Analysis: Efficiently analyzing customer feedback or social media posts.
• Document Processing: Automating invoice extraction, contract review, and expense tracking.
• Healthcare: Summarizing electronic health records, supporting diagnostics, and ensuring data privacy.
• Edge AI: Running on IoT devices for predictive maintenance, anomaly detection, and more.

For more examples, see Data Science Dojo’s AI use cases in industry.

Popular Small Language Models in 2024

Some leading small language models include:

• DistilBERT, TinyBERT, MobileBERT, ALBERT: Lightweight versions of BERT optimized for efficiency.
• Gemma, GPT-4o mini, Granite, Llama 3.2, Ministral, Phi: Modern SLMs from Google, OpenAI, IBM, Meta, Mistral AI, and Microsoft.
• OpenELM, Qwen2, Pythia, SmolLM2: Open-source models designed for on-device and edge deployment.

Explore how Phi-2 achieves surprising performance with minimal parameters

How to Build and Deploy a Small Language Model

1. Choose the Right Model: Start with a pre-trained SLM from platforms like Hugging Face or train your own using domain-specific data.
2. Apply Model Compression: Use pruning, quantization, or knowledge distillation to optimize for your hardware.
3. Fine-Tune for Your Task: Adapt the model to your specific use case with targeted datasets.
4. Deploy Efficiently: Integrate the SLM into your application, leveraging edge devices or on-premises servers for privacy and speed.
5. Monitor and Update: Continuously evaluate performance and retrain as needed to maintain accuracy.

For a step-by-step guide, see Data Science Dojo’s tutorial on fine-tuning language models.

The Future of Small Language Models

As AI adoption accelerates, small language models are expected to become even more capable and widespread. Innovations in model compression, multi-agent systems, and hybrid AI architectures will further enhance their efficiency and applicability. SLMs are not just a cost-saving measure—they represent a strategic shift toward more accessible, sustainable, and privacy-preserving AI.

Frequently Asked Questions (FAQ)

Q: What is a small language model?

A: An AI model with a compact architecture (millions to a few billion parameters) designed for efficient, domain-specific natural language processing tasks.

Q: How do SLMs differ from LLMs?

A: SLMs are smaller, faster, and more cost-effective, ideal for targeted tasks and edge deployment, while LLMs are larger, more versatile, and better for complex, open-ended tasks.

Q: What are the main advantages of small language models?

A: Efficiency, cost-effectiveness, privacy, ease of customization, and sustainability.

Q: Can SLMs be used for real-time applications?

A: Yes, their low latency and resource requirements make them perfect for real-time inference on edge devices.

Q: Are there open-source small language models?

A: Absolutely! Models like DistilBERT, TinyBERT, and Llama 3.2 are open-source and widely used.

Conclusion: Why Small Language Models Matter

Small language models are redefining what’s possible in AI by making advanced language understanding accessible, affordable, and secure. Whether you’re a data scientist, developer, or business leader, now is the time to explore how SLMs can power your next AI project.

Ready to get started?
Explore more on Data Science Dojo’s blog and join our community to stay ahead in the evolving world of AI.

Qwen3 Coder is quickly emerging as one of the most powerful open-source AI models dedicated to code generation and software engineering. Developed by Alibaba’s Qwen team, this model represents a significant leap forward in the field of large language models (LLMs). It integrates an advanced Mixture-of-Experts (MoE) architecture, extensive reinforcement learning post-training, and a massive context window to enable highly intelligent, scalable, and context-aware code generation.

Released in July 2025 under the permissive Apache 2.0 license, Qwen3 Coder is poised to become a foundation model for enterprise-grade AI coding tools, intelligent agents, and automated development pipelines. Whether you’re an AI researcher, developer, or enterprise architect, understanding how Qwen3 Coder works will give you a competitive edge in building next-generation AI-driven software solutions.

What Is Qwen3 Coder?

Qwen3 Coder is a specialized variant of the Qwen3 language model series. It is fine-tuned specifically for programming-related tasks such as code generation, review, translation, documentation, and agentic tool use. What sets it apart is the architectural scalability paired with intelligent behavior in handling multi-step tasks, context-aware planning, and long-horizon code understanding.

Backed by Alibaba’s research in MoE transformers, agentic reinforcement learning, and tool-use integration, Qwen3 Coder is trained on over 7.5 trillion tokens—more than 70% of which are code. It supports over 100 programming and natural languages and has been evaluated on leading benchmarks like SWE-Bench Verified, CodeForces ELO, and LiveCodeBench v5.

Check out this comprehensive guide to large language models

Key Features of Qwen3 Coder

Mixture-of-Experts (MoE) Architecture

Qwen3 Coder’s flagship variant, Qwen3-Coder-480B-A35B-Instruct, employs a 480-billion parameter Mixture-of-Experts transformer. During inference, it activates only 35 billion parameters by selecting 8 out of 160 expert networks. This design drastically reduces computation while retaining accuracy and fluency, enabling enterprises and individual developers to run the model more efficiently.

Reinforcement Learning with Agentic Planning

Qwen3 Coder undergoes post-training with advanced reinforcement learning techniques, including both Code RL and long-horizon RL. It is fine-tuned in over 20,000 parallel environments where it learns to make decisions across multiple steps, handle tools, and interact with browser-like environments. This makes the model highly effective in scenarios like automated pull requests, multi-stage debugging, and planning entire code modules.

Want to take your RAG pipelines to the next level, check out this guide on agentic RAG 

Massive Context Window

One of Qwen3 Coder’s most distinguishing features is its native support for 256,000-token context windows, which can be extended up to 1 million tokens using extrapolation methods like YaRN. This allows the model to process entire code repositories, large documentation files, and interconnected project files in a single pass, enabling deeper understanding and coherence.

Multi-Language and Framework Support

The model supports code generation and translation across a wide range of programming languages including Python, JavaScript, Java, C++, Go, Rust, and many others. It is capable of adapting code between frameworks and converting logic across platforms. This flexibility is critical for organizations that operate in polyglot environments or maintain cross-platform applications.

Developer Integration and Tooling

Qwen3 Coder can be integrated directly into popular IDEs like Visual Studio Code and JetBrains IDEs. It also offers an open-source CLI tool via npm (@qwen-code/qwen-code), which enables seamless access to the model’s capabilities via the terminal. Moreover, Qwen3 Coder supports API-based integration into CI/CD pipelines and internal developer tools.

Documentation and Code Commenting

The model excels at generating inline code comments, README files, and comprehensive API documentation. This ability to translate complex logic into natural language documentation reduces technical debt and ensures consistency across large-scale software projects.

Security Awareness

While Qwen3 Coder is not explicitly trained as a security analyzer, it can identify common software vulnerabilities such as SQL injections, cross-site scripting (XSS), and unsafe function usage. It can also recommend best practices for secure coding, helping developers catch potential issues before deployment.

For a deeper understanding of how finetuning LLMs work, check out this guide

Model Architecture and Training

Qwen3 Coder is built on top of a highly modular transformer architecture optimized for scalability and flexibility. The 480B MoE variant contains 160 expert modules with 62 transformer layers and grouped-query attention mechanisms. Only a fraction of the experts (8 at a time) are active during inference, reducing computational demands significantly.

Training involved a curated dataset of 7.5 trillion tokens, with code accounting for the majority of the training data. The model was trained in both English and multilingual settings and has a solid understanding of natural language programming instructions. After supervised fine-tuning, the model underwent agentic reinforcement learning with thousands of tool-use environments, leading to more grounded, executable, and context-aware code generation.

Benchmark Results

Qwen3 Coder has demonstrated leading performance across a number of open-source and agentic AI benchmarks:

• SWE-Bench Verified: Alibaba reports state-of-the-art performance among open-source models, with no test-time augmentation.

• CodeForces ELO: Qwen3 Coder leads open-source coding models in competitive programming tasks.
• LiveCodeBench v5: Excels at real-world code completion, editing, and translation.
• BFCL Tool Use Benchmarks: Performs reliably in browser-based tool-use environments and multistep reasoning tasks.

Although Alibaba has not publicly released exact pass rate percentages, several independent blogs and early access reports suggest Qwen3 Coder performs comparably to or better than models like Claude Sonnet 4 and GPT-4 on complex multi-turn agentic tasks.

Real-World Applications of Qwen3 Coder

AI Coding Assistants

Developers can integrate Qwen3 Coder into their IDEs or terminal environments to receive live code suggestions, function completions, and documentation summaries. This significantly improves coding speed and reduces the need for repetitive tasks.

Automated Code Review and Debugging

The model can analyze entire codebases to identify inefficiencies, logic bugs, and outdated practices. It can generate pull requests and make suggestions for optimization and refactoring, which is particularly useful in maintaining large legacy codebases.

Multi-Language Development

For teams working in multilingual codebases, Qwen3 Coder can translate code between languages while preserving structure and logic. This includes adapting syntax, optimizing library calls, and reformatting for platform-specific constraints.

Project Documentation

Qwen3 Coder can generate or update technical documentation automatically, producing consistent README files, docstrings, and architectural overviews. This feature is invaluable for onboarding new team members and improving project maintainability.

Secure Code Generation

While not a formal security analysis tool, Qwen3 Coder can help detect and prevent common coding vulnerabilities. Developers can use it to review risky patterns, update insecure dependencies, and implement best security practices across the stack.

Qwen3 Coder vs. Other Coding Models

Getting Started with Qwen3 Coder

Deployment Options:

• Cloud Deployment:

  • Available via Alibaba Cloud Model Studio and OpenRouter for API access.
  • Hugging Face hosts downloadable models for custom deployment.

  Local Deployment:

  • Quantized models (2-bit, 4-bit) can run on high-end workstations.
  • Requires 24GB+ VRAM and 128GB+ RAM for the 480B variant; smaller models available for less powerful hardware.

  CLI and IDE Integration:

  • Qwen Code CLI (npm package) for command-line workflows.
  • Compatible with VS Code, CLINE, and other IDE extensions.

Frequently Asked Questions (FAQ)

Q: What makes Qwen3 Coder different from other LLMs?

A: Qwen3 Coder combines the scalability of MoE, agentic reinforcement learning, and long-context understanding in a single open-source model.

Q: Can I run Qwen3 Coder on my own hardware?

A: Yes. Smaller variants are available for local deployment, including 7B, 14B, and 30B parameter models.

Q: Is the model production-ready?

A: Yes. It has been tested on industry-grade benchmarks and supports integration into development pipelines.

Q: How secure is the model’s output?

A: While not formally audited, Qwen3 Coder offers basic security insights and best practice recommendations.

Conclusion

Qwen3 Coder is redefining what’s possible with open-source AI in software engineering. Its Mixture-of-Experts design, deep reinforcement learning training, and massive context window allow it to tackle the most complex coding challenges. Whether you’re building next-gen dev tools, automating code review, or powering agentic AI systems, Qwen3 Coder delivers the intelligence, scale, and flexibility to accelerate your development process.

For developers and organizations looking to stay ahead in the AI-powered software era, Qwen3 Coder is not just an option—it’s a necessity.

Read more expert insights on Data Science Dojo’s blog.

When building business apps in Power Apps with SharePoint lists as your backend, moving from a development to a production environment involves more than just copying a file. Ensuring your app continues to work seamlessly across environments, especially when using different SharePoint Lists, requires the right migration strategy.

This blog provides a detailed step-by-step guide on how to migrate Power Apps from a development environment to a production environment. It covers two primary methods: the Import/Export Method  and the  Solutions Method including their limitations and when to use each one, with an additional explanation of how Deployment Pipelines  can be used to enhance the Solutions Method for faster and more efficient migrations.  

Discover how to seamlessly automate data flows from Azure Synapse to SharePoint Excel using Power BI and Power Automate.

Import/Export Method 

Overview 

This method involves exporting the Power App as a .zip file from the development environment and importing it into the production environment. It is a straightforward approach but has limitations, especially when dealing with different SharePoint sites for development and production. 

Steps 

Export the Power App: 

• Go to the Power Apps Studio in the development environment. 
• Select the app you want to migrate. 
• Click on File > Save As > Export this app. 
• Save the .zip file to your local machine. 

Import the Power App: 

• Navigate to the Power Apps Studio in the production environment. 
• Click on Apps > Import canvas app. 
• Upload the .zip file exported from the development environment. 

Update Data Sources: 

• If the SharePoint site in the production environment is different from the development environment: 
• Open the imported app in the Power Apps Studio. 
• Go to View > Data sources. 
• Remove the existing SharePoint data sources. 
• Add the new SharePoint data sources from the production environment. 
• If the SharePoint site is the same, no changes are needed. 

Explore a curated collection of resources on integrating SharePoint with modern data and automation tools.

Limitations of the Import/Export Method

• Different SharePoint Sites: If the development and production environments use different SharePoint sites, you must manually update the data sources after importing the app. 
• Manual Effort: This method requires manual intervention, which can be error-prone and time-consuming. 

When to Use The Import/Export Method

• When the development and production environments use the same SharePoint site. 
• For quick, one-off migrations where manual updates are acceptable. 

Find expert insights and tutorials on ETL tools for efficient data extraction, transformation, and loading.

Solutions Method 

Overview 

The Solutions Method is a more robust approach that leverages Power Platform solutions to package and migrate apps, flows, and related components. It is ideal for environments with separate SharePoint sites for development and production. 

Steps 

Step 1: Prepare SharePoint Lists in Production 

Export SharePoint Lists from Development: 

• Go to the SharePoint site in the development environment. 
• Export each list as a .csv file with schema. 
• For lists with lookup columns (e.g., “Lot Specifications” list with a lookup to “Submissions” list), import the “Lot Specifications” list first. Then, import the “Submissions” list. After importing the data type of lookup column would have changed therefore, delete and re-add the ‘Submissions Lookup’ column in the “Lot Specifications” list to reference the ID field in the “Submissions” list. 

Import SharePoint Lists into Production: 

• Go to the SharePoint site in the production environment. 
• Import each list using the .csv files exported from the development environment. 
• If there are lookup columns, ensure the referenced lists are imported first. 
• After importing, re-create any lookup columns manually if necessary. 

Empower your analytics journey with guides and tips on self-service business intelligence solutions.

Step 2: Create a Solution in Development 

Create a New Solution: 

• Go to the Power Apps Studio in the development environment. 
• Navigate to Solutions > New Solution. 
• Provide a name and publisher for the solution. 

Add the Canvas App to the Solution: 

• In the solution, click Add Existing >App> Canvas App. 
• Select the app you want to migrate. 

Create Environment Variables: 

• In the solution, click New > Environment Variable. 

• Set the data type to Data Source and the connector to SharePoint. 
• Use personal or service account (recommended) for the connection. 
• Set the parameter type to Site and provide the SharePoint site URL for the development environment. 

Unlock best practices and automation strategies for advanced Excel reporting and data visualization.

Update Data Sources in the App: 

• Open the app within the solution. 
• Remove the existing SharePoint data sources. 
• Add the SharePoint data source again, but this time use the environment variable for the site URL. 

• Select all the lists, in the background environment variables for selected Lists will get created. 

 

Step 3: Deploy the Solution to Production 

Create a Production Environment: 

• Go to the Power Platform Admin Center. 
• Create a new environment with the Production type. 
• Ensure Dataverse is enabled (required for solutions). 

Deploy the Solution: 

• In the development environment, go to Solutions. 
• Select the Pipelines Tab and add new pipeline by selecting the Target environment. 

• Set up stages for development, testing, and production as needed. 
• Run the pipeline to deploy the solution from development to production. 
• Configure environment variables. 
• Monitor the deployment process for any issues and validate that the app functions correctly in the production environment. 

Step 4: Update Power Automate Flows 

Duplicate Flows for Production: 

• Go to Power Automate in the development environment. 
• Duplicate any flows associated with the app. 
• Update the SharePoint site and lists in the duplicated flows to point to the production environment. 

Test the Flows: 

• Ensure the flows work correctly with the production data sources. 

Advantages of The Solutions Method

• Automated Deployment: Solutions allow for automated deployment of apps and components. 
• Environment Variables: Environment variables make it easier to manage different data sources across environments. 
• Scalability: Ideal for complex projects with multiple apps and flows. 

When to Use The Solutions Method

• When the development and production environments use different SharePoint sites. 
• For projects requiring repeatable and scalable deployment processes. 

Final Thoughts 

Migrating SharePoint-based Power Apps to production doesn’t have to be daunting. Whether you’re a solo maker or an enterprise team, there’s a method that fits your needs. The key is choosing the right one based on your environment structure, need for automation, and long-term maintainability. 

Ready to take your skills to the next level?
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