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prompt engineering

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.

Naive RAG vs Graph RAG illustrated

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?

entity relationship graph
source: Langchain

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

GraohRAG vs RAG

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.
Vector Database vs Knowledge Graphs
source: AI Planet

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

Graph RAG implementation
source: infogain
  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.

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August 7, 2025

Vibe coding is revolutionizing the way we approach software development. At its core, vibe coding means expressing your intent in natural language and letting AI coding assistants translate that intent into working code. Instead of sweating the syntax, you describe the “vibe” of what you want—be it a data pipeline, a web app, or an analytics automation script—and frameworks like Replit, GitHub Copilot, Gemini Code Assist, and others do the heavy lifting.

This blog will guide you through what vibe coding is, why it matters, its benefits and limitations, and a deep dive into the frameworks making it possible. Whether you’re a data engineer, software developer, or just AI-curious, you’ll discover how prompt engineering, large language models, and rapid prototyping are reshaping the future of software development.

What Is Vibe Coding?

Vibe coding is a new paradigm in software development where you use natural language programming to instruct AI coding assistants to generate, modify, and even debug code. The term, popularized by AI thought leaders like Andrej Karpathy, captures the shift from manual coding to intent-driven development powered by large language models (LLMs) such as GPT-4, Gemini, and Claude.

How does vibe coding work?

  • You describe your goal in plain English (e.g., “Build a REST API for customer management in Python”).
  • The AI coding assistant interprets your prompt and generates the code.
  • You review, refine, and iterate—often using further prompts to tweak or extend the solution.

This approach leverages advances in prompt engineering, code generation, and analytics automation, making software development more accessible and efficient than ever before.

Learn more about LLMs and their applications in this Data Science Dojo guide.

Top Vibe Coding Frameworks

The Benefits of Vibe Coding

1. Accelerated Rapid Prototyping

Vibe coding enables you to move from idea to prototype in minutes. By using natural language programming, you can quickly test concepts, automate analytics, or build MVPs without getting bogged down in boilerplate code.

2. Lower Barrier to Entry

AI coding assistants democratize software development. Non-developers, data analysts, and business users can now participate in building solutions, thanks to intuitive prompt engineering and low-code interfaces.

3. Enhanced Productivity

Developers can focus on high-level architecture and problem-solving, letting AI handle repetitive or routine code generation. This shift boosts productivity and allows teams to iterate faster.

4. Consistency and Best Practices

Many frameworks embed best practices and patterns into their code generation, helping teams maintain consistency and reduce errors.

5. Seamless Integration with Data Engineering and Analytics Automation

Vibe coding is especially powerful for data engineering tasks—think ETL pipelines, data validation, and analytics automation—where describing workflows in natural language can save hours of manual coding.

For more on how AI is transforming workflows, see How AI is Transforming Data Science Workflows.

The Frameworks Powering Vibe Coding

Let’s explore the leading frameworks and tools that make vibe coding possible. Each brings unique strengths to the table, enabling everything from code generation to analytics automation and low-code development.

Replit

Top vibe coding framework - Replit
source: Replit

Replit is a cloud-based development environment that brings vibe coding to life. Its Ghostwriter AI coding assistant allows you to describe what you want in natural language, and it generates code, suggests improvements, and even helps debug. Replit supports dozens of languages and is ideal for rapid prototyping, collaborative coding, and educational use.

  • Key Features: Real-time code generation, multi-language support, collaborative editing, and instant deployment.
  • Use Case: “Create a Python script to scrape weather data and visualize it”—Ghostwriter handles the rest.

Learn more at Replit.

GitHub Copilot

Top vibe coding framework - Github Copilot
source: Github

GitHub Copilot, is an AI coding assistant that integrates directly into your IDE (like VS Code). It offers real-time code suggestions, autocompletes functions, and can even generate entire modules from a prompt. Copilot excels at code generation for software development, data engineering, and analytics automation.

  • Key Features: Inline code suggestions, support for dozens of languages, context-aware completions, and integration with popular IDEs.
  • Use Case: “Write a function to clean and merge two dataframes in pandas”—Copilot generates the code as you type.

Explore more at GitHub Copilot.

Gemini Code Assist

Top vibe coding framework - Gemini Code Assist
source: Google

Gemini Code Assist is Google’s AI-powered coding partner, designed to help developers write, understand, and optimize code using natural language programming. It’s particularly strong in analytics automation and data engineering, offering smart code completions, explanations, and refactoring suggestions.

  • Key Features: Context-aware code generation, integration with Google Cloud, and support for prompt-driven analytics workflows.
  • Use Case: “Build a data pipeline that ingests CSV files from Google Cloud Storage and loads them into BigQuery.”

Learn more at Gemini Code Assist.

Cursor

Top vibe coding framework - Cursor Ai
source: Cursor

Cursor is an AI-powered IDE built from the ground up for vibe coding. It enables developers to write prompts, generate code, and iterate—all within a seamless, collaborative environment. Cursor is ideal for rapid prototyping, low-code development, and team-based software projects.

  • Key Features: Prompt-driven code generation, collaborative editing, and integration with popular version control systems.
  • Use Case: “Generate a REST API in Node.js with endpoints for user authentication and data retrieval.”

Discover Cursor at Cursor.

OpenAI Codex

Top vibe coding framework - Openai Codex
source: Openai

OpenAI Codex is the engine behind many AI coding assistants, including GitHub Copilot and ChatGPT. It’s a large language model trained specifically for code generation, supporting dozens of programming languages and frameworks.

  • Key Features: Deep code understanding, multi-language support, and integration with various development tools.
  • Use Case: “Translate this JavaScript function into Python and optimize for performance.”

Read more about Codex at OpenAI Codex.

IBM watsonx Code Assistant

IBM watsonx Code Assistant is an enterprise-grade AI coding assistant designed for analytics automation, data engineering, and software development. It offers advanced prompt engineering capabilities, supports regulatory compliance, and integrates with IBM’s cloud ecosystem.

  • Key Features: Enterprise security, compliance features, support for analytics workflows, and integration with IBM Cloud.
  • Use Case: “Automate ETL processes for financial data and generate audit-ready logs.”

Explore IBM watsonx Code Assistant at IBM.

How Vibe Coding Empowers Data Engineering and Analytics Automation

Vibe coding isn’t just for web apps or simple scripts—it’s a game-changer for data engineering and analytics automation. Here’s how:

  • ETL Pipelines: Describe your data flow in natural language, and let AI generate the code to extract, transform, and load data.
  • Analytics Automation: Automate reporting, dashboard creation, and data validation with prompt-driven workflows.
  • Rapid Prototyping: Test new data models, algorithms, or analytics strategies in minutes, not days.

See how Context Engineering shapes reliable, context-aware LLM outputs.

The Limitations of Vibe Coding

While vibe coding is a game-changer, it’s not without challenges:

  • Code Quality and Reliability: AI-generated code may contain subtle bugs or inefficiencies. Always review and test before deploying.
  • Debugging Complexity: If you don’t understand the generated code, troubleshooting can be tough.
  • Security Risks: AI may inadvertently introduce vulnerabilities. Human oversight is essential.
  • Scalability: Vibe coding excels at rapid prototyping and automation, but complex, large-scale systems still require traditional software engineering expertise.
  • Over-Reliance on AI: Relying solely on AI coding assistants can erode foundational coding skills over time.

For a deep dive into prompt engineering and its importance, check out Master Prompt Engineering: Proven Strategies and Hands-On Examples.

Best Practices for Effective Vibe Coding

  1. Be Specific with Prompts: Clear, detailed instructions yield better results.
  2. Iterate and Refine: Use feedback loops to improve code quality.
  3. Review and Test: Always validate AI-generated code for correctness and security.
  4. Document Your Work: Maintain clear documentation for future maintenance.
  5. Stay Involved: Use AI as a copilot, not a replacement for human expertise.

For hands-on strategies, check out Strategies to master prompt engineering by hands-on examples.

The Future of Vibe Coding

As large language models and AI coding assistants continue to evolve, vibe coding will become the default for:

  • Internal tool creation
  • Business logic scripting
  • Data engineering automation
  • Low-code/no-code backend assembly

Emerging trends include multimodal programming (voice, text, and visual), agentic AI for workflow orchestration, and seamless integration with cloud platforms.

Stay updated with the latest trends in Agentic AI.

Frequently Asked Questions (FAQs)

Q1: Is vibe coding replacing traditional programming?

No—it augments it. Developers still need to review, refine, and understand the code.

Q2: Can vibe coding be used for production systems?

Yes, with proper validation, testing, and reviews. AI can scaffold, but humans should own the last mile.

Q3: What languages and frameworks does vibe coding support?

Virtually all popular languages (Python, JavaScript, SQL) and frameworks (Django, React, dbt, etc.).

Q4: How can I start vibe coding today?

Try tools like Replit, GitHub Copilot, Gemini Code Assist, or ChatGPT. Start with small prompts and iterate.

Q5: What are the limitations of vibe coding?

Best for prototyping and automation; complex systems still require traditional expertise.

Conclusion & Next Steps

Vibe coding is more than a trend—it’s a fundamental shift in how we build software. By leveraging AI coding assistants, prompt engineering, and frameworks like Replit, GitHub Copilot, Gemini Code Assist, Cursor, ChatGPT, Claude, OpenAI Codex, and IBM watsonx Code Assistant, you can unlock new levels of productivity, creativity, and accessibility in software development.

Ready to try vibe coding?

  • Explore the frameworks above and experiment with prompt-driven development.
  • Dive deeper into prompt engineering and AI-powered workflows on Data Science Dojo’s blog.

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July 24, 2025

How do LLMs work? It’s a question that sits at the heart of modern AI innovation. From writing assistants and chatbots to code generators and search engines, large language models (LLMs) are transforming the way machines interact with human language. Every time you type a prompt into ChatGPT or any other LLM-based tool, you’re initiating a complex pipeline of mathematical and neural processes that unfold within milliseconds.

In this post, we’ll break down exactly how LLMs work, exploring every critical stage, tokenization, embedding, transformer architecture, attention mechanisms, inference, and output generation. Whether you’re an AI engineer, data scientist, or tech-savvy reader, this guide is your comprehensive roadmap to the inner workings of LLMs.

What Is a Large Language Model?

A large language model (LLM) is a deep neural network trained on vast amounts of text data to understand and generate human-like language. These models are the engine behind AI applications such as ChatGPT, Claude, LLaMA, and Gemini. But to truly grasp how LLMs work, you need to understand the architecture that powers them: the transformer model.

Key Characteristics of LLMs:

  • Built on transformer architecture
  • Trained on large corpora using self-supervised learning
  • Capable of understanding context, semantics, grammar, and even logic
  • Scalable and general-purpose, making them adaptable across tasks and industries

Learn more about LLMs and their applications.

Why It’s Important to Understand How LLMs Work

LLMs are no longer just research experiments, they’re tools being deployed in real-world settings across finance, healthcare, customer service, education, and software development. Knowing how LLMs work helps you:

  • Design better prompts
  • Choose the right models for your use case
  • Understand their limitations
  • Mitigate risks like hallucinations or bias
  • Fine-tune or integrate LLMs more effectively into your workflow

Now, let’s explore the full pipeline of how LLMs work, from input to output.

7 Best Large Language Models (LLMs) You Must Know About

Step-by-Step: How Do LLMs Work?

Step 1: Tokenization – How do LLMs work at the input stage?

The first step in how LLMs work is tokenization. This is the process of breaking raw input text into smaller units called tokens. Tokens may represent entire words, parts of words (subwords), or even individual characters.

Tokenization serves two purposes:

  1. It standardizes inputs for the model.
  2. It allows the model to operate on a manageable vocabulary size.

Different models use different tokenization schemes (Byte Pair Encoding, SentencePiece, etc.), and understanding them is key to understanding how LLMs work effectively on multilingual and domain-specific text.

Tokenization

Explore a hands-on curriculum that helps you build custom LLM applications!

Step 2: Embedding – How do LLMs work with tokens?

Once the input is tokenized, each token is mapped to a high-dimensional vector through an embedding layer. These embeddings capture the semantic and syntactic meaning of the token in a numerical format that neural networks can process.

However, since transformers (the architecture behind LLMs) don’t have any inherent understanding of sequence or order, positional encodings are added to each token embedding. These encodings inject information about the position of each token in the sequence, allowing the model to differentiate between “the cat sat on the mat” and “the mat sat on the cat.”

This combined representation—token embedding + positional encoding—is what the model uses to begin making sense of language structure and meaning. During training, the model learns to adjust these embeddings so that semantically related tokens (like “king” and “queen”) end up with similar vector representations, while unrelated tokens remain distant in the embedding space.

How embeddings work

Step 3: Transformer Architecture – How do LLMs work internally?

At the heart of how LLMs work is the transformer architecture, introduced in the 2017 paper Attention Is All You Need. The transformer is a sequence-to-sequence model that processes entire input sequences in parallel—unlike RNNs, which work sequentially.

Key Components:
  • Multi-head self-attention: Enables the model to focus on relevant parts of the input.
  • Feedforward neural networks: Process attention outputs into meaningful transformations.
  • Layer normalization and residual connections: Improve training stability and gradient flow.

The transformer’s layered structure, often with dozens or hundreds of layers—is one of the reasons LLMs can model complex patterns and long-range dependencies in text.

Transformer architecture

Step 4: Attention Mechanisms – How do LLMs work to understand context?

If you want to understand how LLMs work, you must understand attention mechanisms.

Attention allows the model to determine how much focus to place on each token in the sequence, relative to others. In self-attention, each token looks at all other tokens to decide what to pay attention to.

For example, in the sentence “The cat sat on the mat because it was tired,” the word “it” likely refers to “cat.” Attention mechanisms help the model resolve this ambiguity.

Types of Attention in LLMs:
  • Self-attention: Token-to-token relationships within a single sequence.
  • Cross-attention (in encoder-decoder models): Linking input and output sequences.
  • Multi-head attention: Several attention layers run in parallel to capture multiple relationships.

Attention is arguably the most critical component in how LLMs work, enabling them to capture complex, hierarchical meaning in language.

 

LLM Finance: The Impact of Large Language Models in Finance

Step 5: Inference – How do LLMs work during prediction?

During inference, the model applies the patterns it learned during training to generate predictions. This is the decision-making phase of how LLMs work.

Here’s how inference unfolds:

  1. The model takes the embedded input sequence and processes it through all transformer layers.

  2. At each step, it outputs a probability distribution over the vocabulary.

  3. The most likely token is selected using a decoding strategy:

    • Greedy search (pick the top token)

    • Top-k sampling (pick from top-k tokens)

    • Nucleus sampling (top-p)

  4. The selected token is fed back into the model to predict the next one.

This token-by-token generation continues until an end-of-sequence token or maximum length is reached.

Token prediction

Step 6: Output Generation – From Vectors Back to Text

Once the model has predicted the entire token sequence, the final step in how LLMs work is detokenization—converting tokens back into human-readable text.

Output generation can be fine-tuned through temperature and top-p values, which control randomness and creativity. Lower temperature values make outputs more deterministic; higher values increase diversity.

How to Tune LLM Parameters for Optimal Performance

Prompt Engineering: A Critical Factor in How LLMs Work

Knowing how LLMs work is incomplete without discussing prompt engineering—the practice of crafting input prompts that guide the model toward better outputs.

Because LLMs are highly context-dependent, the structure, tone, and even punctuation of your prompt can significantly influence results.

Effective Prompting Techniques:

  1. Use examples (few-shot or zero-shot learning)
  2. Give explicit instructions
  3. Set role-based context (“You are a legal expert…”)
  4. Add delimiters to structure content clearly

Mastering prompt engineering is a powerful way to control how LLMs work for your specific use case.

Learn more about prompt engineering strategies.

How Do LLMs Work Across Modalities?

While LLMs started in text, the principles of how LLMs work are now being applied across other data types—images, audio, video, and even robotic actions.

Examples:

  • Code generation: GitHub Copilot uses LLMs to autocomplete code.
  • Vision-language models: Combine image inputs with text outputs (e.g., GPT-4V).
  • Tool-using agents: Agentic AI systems use LLMs to decide when to call tools like search engines or APIs.

Understanding how LLMs work across modalities allows us to envision their role in fully autonomous systems.

Explore top LLM use cases across industries.

Summary Table: How Do LLMs Work?

How do LLMs work?

Frequently Asked Questions

Q1: How do LLMs work differently from traditional NLP models?

Traditional models like RNNs process inputs sequentially, which limits their ability to retain long-range context. LLMs use transformers and attention to process sequences in parallel, greatly improving performance.

Q2: How do embeddings contribute to how LLMs work?

Embeddings turn tokens into mathematical vectors, enabling the model to recognize semantic relationships and perform operations like similarity comparisons or analogy reasoning.

Q3: How do LLMs work to generate long responses?

They generate one token at a time, feeding each predicted token back as input, continuing until a stopping condition is met.

Q4: Can LLMs be fine-tuned?

Yes. Developers can fine-tune pretrained LLMs on specific datasets to specialize them for tasks like legal document analysis, customer support, or financial forecasting. Learn more in Fine-Tuning LLMs 101

Q5: What are the limitations of how LLMs work?

LLMs may hallucinate facts, lack true reasoning, and can be sensitive to prompt structure. Their outputs reflect patterns in training data, not grounded understanding. Learn more in Cracks in the Facade: Flaws of LLMs in Human-Computer Interactions

Conclusion: Why You Should Understand How LLMs Work

Understanding how LLMs work helps you unlock their full potential, from building smarter AI systems to designing better prompts. Each stage—tokenization, embedding, attention, inference, and output generation—plays a unique role in shaping the model’s behavior.

Whether you’re just getting started with AI or deploying LLMs in production, knowing how LLMs work equips you to innovate responsibly and effectively.

Ready to dive deeper?

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July 23, 2025

Retrieval-augmented generation (RAG) has already reshaped how large language models (LLMs) interact with knowledge. But now, we’re witnessing a new evolution: the rise of RAG agents—autonomous systems that don’t just retrieve information, but plan, reason, and act.

In this guide, we’ll walk through what a rag agent actually is, how it differs from standard RAG setups, and why this new paradigm is redefining intelligent problem-solving.

Want to dive deeper into agentic AI? Explore our full breakdown in this blog.

What is Agentic RAG?

At its core, agentic rag (short for agentic retrieval-augmented generation) combines traditional RAG methods with the decision-making and autonomy of AI agents.

While classic RAG systems retrieve relevant knowledge to improve the responses of LLMs, they remain largely reactive, they answer what you ask but don’t think ahead. A rag agent pushes beyond this. It autonomously breaks down tasks, plans multiple reasoning steps, and dynamically interacts with tools, APIs, and multiple data sources—all with minimal human oversight.

In short: agentic rag isn’t just answering questions; it’s solving problems.

RAG vs Self RAG vs Agentic RAG
source: Medium

Discover how retrieval-augmented generation supercharges large language models, improving response accuracy and contextual relevance without retraining.

Standard RAG vs. Agentic RAG: What’s the Real Difference?

How Standard RAG Works

Standard RAG pairs an LLM with a retrieval system, usually a vector database, to ground its responses in real-world, up-to-date information. Here’s what typically happens:

  1. Retrieval: Query embeddings are matched against a vector store to pull in relevant documents.

  2. Augmentation: These documents are added to the prompt context.

  3. Generation: The LLM uses the combined context to generate a more accurate, grounded answer.

This flow works well, especially for answering straightforward questions or summarizing known facts. But it’s fundamentally single-shot—there’s no planning, no iteration, no reasoning loop.

Curious about whether to finetune or use RAG for your AI applications? This breakdown compares both strategies to help you choose the best path forward.

How Agentic RAG Steps It Up

Agentic RAG injects autonomy into the process. Now, you’re not just retrieving information, you’re orchestrating an intelligent agent to:

  • Break down queries into logical sub-tasks.

  • Strategize which tools or APIs to invoke.

  • Pull data from multiple knowledge bases.

  • Iterate on outputs, validating them step-by-step.

  • Incorporate multimodal data when needed (text, images, even structured tables).

Here’s how the two stack up:

Standard RAg vs RAG agent

Technical Architecture of Rag Agents

Let’s break down the tech stack that powers rag agents.

Core Components

  • AI Agent Framework: The backbone that handles planning, memory, task decomposition, and action sequencing. Common tools: LangChain, LlamaIndex, LangGraph.

  • Retriever Module: Connects to vector stores or hybrid search systems (dense + sparse) to fetch relevant content.

  • Generator Model: A large language model like GPT-4, Claude, or T5, used to synthesize and articulate final responses.

  • Tool Calling Engine: Interfaces with APIs, databases, webhooks, or code execution environments.

  • Feedback Loop: Incorporates user feedback and internal evaluation to improve future performance.

How It All Comes Together

  1. User submits a query say, “Compare recent trends in GenAI investments across Asia and Europe.”

  2. The rag agent plans its approach: decompose the request, decide on sources (news APIs, financial reports), and select retrieval strategy.

  3. It retrieves data from multiple sources—maybe some from a vector DB, others from structured APIs.

  4. It iterates, verifying facts, checking for inconsistencies, and possibly calling a summarization tool.

  5. It returns a comprehensive, validated answer—possibly with charts, structured data, or follow-up recommendations.

RAG Agent

Learn about the common pitfalls and technical hurdles of deploying RAG pipelines—and how to overcome them in real-world systems.

Benefits of Agentic RAG

Why go through the added complexity of building rag agents? Because they unlock next-level capabilities:

  • Flexibility: Handle multi-step, non-linear workflows that mimic human problem-solving.

  • Accuracy: Validate intermediate outputs, reducing hallucinations and misinterpretations.

  • Scalability: Multiple agents can collaborate in parallel—ideal for enterprise-scale workflows.

  • Multimodality: Support for image, text, code, and tabular data.

  • Continuous Learning: Through memory and feedback loops, agents improve with time and use.

Challenges and Considerations

Of course, this power comes with trade-offs:

  • System Complexity: Orchestrating agents, tools, retrievers, and LLMs can introduce fragility.

  • Compute Costs: More retrieval steps and more tool calls mean higher resource use.

  • Latency: Multi-step processes can be slower than simple RAG flows.

  • Reliability: Agents may fail, loop indefinitely, or return conflicting results.

  • Data Dependency: Poor-quality data or sparse knowledge bases degrade agent performance.

Rag agents are incredibly capable, but they require careful engineering and observability.

Real-World Use Cases

1. Enterprise Knowledge Retrieval

Employees can use rag agents to pull data from CRMs, internal wikis, reports, and dashboards—then get a synthesized answer or auto-generated summary.

2. Customer Support Automation

Instead of simple chatbots, imagine agents that retrieve past support tickets, call refund APIs, and escalate intelligently based on sentiment.

3. Healthcare Intelligence

Rag agents can combine patient history, treatment guidelines, and the latest research to suggest evidence-based interventions.

4. Business Intelligence

From competitor benchmarking to KPI tracking, rag agents can dynamically build reports across multiple structured and unstructured data sources.

5. Adaptive Learning Tools

Tutoring agents can adjust difficulty levels, retrieve learning material, and provide instant feedback based on a student’s knowledge gaps.

RAG Agent workflow
Langchain

Explore how context engineering is reshaping prompt design, retrieval quality, and system reliability in next-gen RAG and agentic systems.

Future Trends in Agentic RAG Technology

Here’s where the field is heading:

  • Multi-Agent Collaboration: Agents that pass tasks to each other—like departments in a company.

  • Open Source Growth: Community-backed frameworks like LangGraph and LlamaIndex are becoming more powerful and modular.

  • Verticalized Agents: Domain-specific rag agents for law, finance, medicine, and more.

  • Improved Observability: Tools for debugging reasoning chains and understanding agent behavior.

  • Responsible AI: Built-in mechanisms to ensure fairness, interpretability, and compliance.

Conclusion & Next Steps

Rag agents are more than an upgrade to RAG—they’re a new class of intelligent systems. By merging retrieval, reasoning, and tool execution into one autonomous workflow, they bridge the gap between passive Q&A and active problem-solving.

If you’re looking to build AI systems that don’t just answer but truly act—this is the direction to explore.

Next steps:

Frequently Asked Questions (FAQ)

Q1: What is a agentic rag?

Agentic rag combines retrieval-augmented generation with multi-step planning, memory, and tool usage—allowing it to autonomously tackle complex tasks.

Q2: How does agentic RAG differ from standard RAG?

Standard RAG retrieves documents and augments the LLM prompt. Agentic RAG adds reasoning, planning, memory, and tool calling—making the system autonomous and iterative.

Q3: What are the benefits of rag agents?

Greater adaptability, higher accuracy, multi-step reasoning, and the ability to operate across modalities and APIs.

Q4: What challenges should I be aware of?

Increased complexity, higher compute costs, and the need for strong observability and quality data.

Q5: Where can I learn more?

Start with open-source tools like LangChain and LlamaIndex, and explore educational content from Data Science Dojo and beyond.

July 21, 2025

Large language models (LLMs) are trained on massive textual data to generate creative and contextually relevant content. Since enterprises are utilizing LLMs to handle information effectively, they must understand the structure behind these powerful tools and the challenges associated with them.

One such component worthy of attention is the llm context window. It plays a crucial role in the development and evolution of LLM technology to enhance the way users interact with information.

 

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In this blog, we will navigate the paradox around LLM context windows and explore possible solutions to overcome the challenges associated with large context windows. However, before we dig deeper into the topic, it’s essential to understand what LLM context windows are and their importance in the world of language models.

What Are LLM Context Windows?

An LLM context window acts like a lens providing perspective to a large language model. The window keeps shifting to ensure a constant flow of information for an LLM as it engages with the user’s prompts and inputs. Thus, it becomes a short-term memory for LLMs to access when generating outputs.

 

Understanding the llm context window
A visual to explain context windows – Source: TechTarget

 

The functionality of a context window can be summarized through the following three aspects:

  • Focal word – Focuses on a particular word and the surrounding text, usually including a few nearby sentences in the data
  • Contextual information – Interprets the meaning and relationship between words to understand the context and provide relevant output for the users
  • Window size – Determines the amount of data and contextual information that is quickly accessible to the LLM when generating a response

Thus, context windows bae their function on the above aspects to assist LLMs in creating relevant and accurate outputs. These aspects also lay down a basis for the context window paradox that we aim to explore here.

 

Crack the Large Language Model’s code

 

What Is the Context Window Paradox?

It is a dilemma that revolves around the size of context windows. While it is only logical to expect large context windows to be beneficial, there are two sides to this argument.

 

Join Jerry Liu, CEO of LlamaIndex, as he simplifies the Curse of Dimensionality, Context Window Paradox, and more in LLMs.

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Side One

It elaborates on the benefits of large context windows. With a wider lens, LLMs get access to more textual data and information. It enables an LLM to study more data, forming better connections between words and generating improved contextual information.

Thus, the LLM generates enhanced outputs with better understanding and a coherent flow of information. It also assists language models to handle complex tasks more efficiently.

 

You might also want to learn about LLM hallucinations 

 

Side Two

While larger windows give access to more contextual information, it also increases the amount of data for LLMs to process. It makes it challenging to identify useful knowledge from irrelevant details in large amounts of data, overwhelming LLMs at the cost of degraded performance.

Thus, it makes the size of LLM context windows a paradoxical matter where users have to look for the right trade-off between improved contextual information and the high performance of LLMs. It leads one to decide how much information is a good amount for an efficient LLM.

Before we elaborate further on the paradox, let’s understand the role and importance of context windows in LLMs.

 

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Why Do Context Windows Matter in LLMs?

LLM context windows are important in ensuring the efficient working of LLMs. Their multifaceted role is described below.

Understanding Language Nuances

The focused perspective of context windows provides surrounding information in data, enabling LLMs to better understand the nuances of language. The model becomes trained to grasp the meaning and intent behind words. It empowers an LLM to perform the following tasks:

Machine Translation

An LLM uses a context window to identify the nuances of language and contextual information to create the most appropriate translation. It caters to the understanding of context within an entire sentence or paragraph to ensure efficient machine translation.

Question Answering

Understanding contextual information is crucial when answering questions. With relevant information on the situation and setting, it is easier to generate an informative answer. Using a context window, LLMs can identify the relevant parts of the conversation and avoid irrelevant tangents.

 

Also learn how you can generate code with LLMs

 

Coherent Text Generation

LLMs use context windows to generate text that aligns with the preceding information. By analyzing the context, the model can maintain coherence, tone, and overall theme in its response. This is important for tasks like:

Chatbots

Conversational engagement relies on a high level of coherence. It is particularly used in chatbots where the model remembers past interactions within a conversation. With the use of context windows, a chatbot can create a more natural and engaging conversation.

Here’s a step-by-step guide to building LLM chatbots.

 

 

Creative Textual Responses

LLMs can create creative content like poems, essays, and other texts. A context window allows an LLM to understand the desired style and theme from the given dataset to create creative responses that are more relevant and accurate.

Contextual Learning

Context is a crucial element for LLMs which becomes more accessible with context windows. Analyzing the relevant data with a focus on words and text of interest allows an LLM to learn and adapt their responses. It becomes useful for uses like:

Virtual Assistants

Virtual assistants are designed to help users in real time. Context window enables the assistant to remember past requests and preferences to provide more personalized and helpful service.

Open-Ended Dialogues

In ongoing conversations, the context window allows the LLM to track the flow of the dialogue and tailor its responses accordingly.

Hence, context windows act as a lens through which LLMs view and interpret information. The size and effectiveness of this perspective significantly impact the LLM’s ability to understand and respond to language in a meaningful way. This brings us back to the size of a context window and the associated paradox.

Read bout chatbots in detail

The Context Window Paradox: Is Bigger, Not Better?

 

Why Bigger Context Windows Aren't Always Better?

 

While a bigger context window ensures LLM’s access to more information and better details for contextual relevance, it comes at a cost. Let’s take a look at some of the drawbacks for LLMs that come with increasing the context window size.

Information Overload

Too much information can overwhelm a language model just like humans. Too much text leads to an information overload that includes irrelevant information that can become a distraction for an LLM.

It makes it difficult for LLMs to focus on key knowledge aspects within the context, making it difficult to generate effective responses to queries. Moreover, a large textual dataset also requires more computational resources, resulting in more expense and slower LLM performance.

Getting Lost in Data

Even with a larger window for data access, an LLM can process limited information effectively. In a wider span of data, an LLM can focus on the edges. It results in LLMs prioritizing the data at the start and end of a window, missing out on important information in the middle.

Moreover, mismanaged truncation to fit a large window size can result in the loss of essential information. As a result, it can compromise the quality of the results produced by the LLM.

 

Also learn how to visualize data

 

Poor Information Management

A wider LLM context window means a larger context that can lead to poor handling and management of information or data. With too much noise in the data, it becomes difficult for an LLM to differentiate between important and unimportant information.

It can create redundancy or contradictions in produced results, harming the credibility and efficiency of a large language model. Moreover, it creates a possibility for bias amplification, leading to misleading outputs.

Long-Range Dependencies

With a focus on concepts spread far apart in large context windows, it can become challenging for an LLM to understand relationships between words and concepts. It limits the LLM’s ability for tasks requiring historical analysis or cause-and-effect relationships.

Thus, large context windows offer advantages but with some limitations. The best approach is to find the right balance between context size, efficiency, and the specific task at hand is crucial for optimal LLM performance.

 

How generative AI and LLMs work

 

Techniques to Address Context Window Paradox

Let’s look at some techniques that can assist you in optimizing the use of large context windows. Each one explores ways to find the optimal balance between context size and LLM performance.

Prioritization and Attention Mechanisms

Attention mechanism techniques can be used to focus on crucial and most relevant information within a context window. Hence, an LLM does not have to deal with the entire flow of information and can only focus on the highlighted parts within the window, enhancing its overall performance.

 

A detailed guide on attention mechanism in NLP

 

Strategic Truncation

Since all the information within a context window is not important or equally relevant, truncation can be used to strategically remove unrelated details. The core elements of the text needed for the task are preserved while the unnecessary information is removed, avoiding information overload on the LLM.

 

 

Retrieval Augmented Generation (RAG)

This technique integrates an LLM with a retrieval system containing a vast external knowledge base to find information specifically relevant to the current prompt and context window. This allows the LLM to access a wider range of information without being overwhelmed by a massive internal window.

 

Another interesting read: RAG in LLM

 

 

 

Prompt Engineering

It focuses on crafting clear instructions for the LLM to efficiently utilize the context window. Clear and focused prompts can guide the LLM toward relevant information within the context, enhancing the LLM’s efficiency in utilizing context windows.

 

Here’s a 10-step guide to becoming a prompt engineer

 

Optimizing Training Data

It is a useful practice to organize training data, creating well-defined sections, summaries, and clear topic shifts, helping the LLM learn to navigate larger contexts more effectively. The structured information makes it easier for an LLM to process data within the context window.

These techniques can help us address the context window paradox and leverage the benefits of larger context windows while mitigating their drawbacks.

The Future of Context Windows in LLMs

We have looked at the varying aspects of LLM context windows and the paradox involving their size. With the right approach, technique, and balance, it is possible to choose the optimal context window size for an LLM. Moreover, it also highlights the need to focus on the potential of context windows beyond the paradox around their size.

The future is expected to transition from cramming more information into a context window to ward smarter context utilization. Moreover, advancements in attention mechanisms and integration with external knowledge bases will also play a role, allowing LLMs to pinpoint truly relevant information regardless of window size.

 

Explore a hands-on curriculum that helps you build custom LLM applications! 

 

Ultimately, the goal is for LLMs to become context masters, understanding not just the “what” but also the “why” within the information they process. This will pave the way for LLMs to tackle even more intricate tasks and generate responses that are both informative and human-like.

April 22, 2024

Prompt engineering is the process of designing and refining prompts that are given to large language models (LLMs) to get them to generate the desired output.

The beginning of prompt engineering

The history of prompt engineering can be traced back to the early days of artificial intelligence when researchers were experimenting with ways to get computers to understand and respond to natural language.

Learn in detail about —> Prompt Engineering

Best practices for prompt engineering
Best practices for prompt engineering

One of the earliest examples of prompt engineering was the work of Terry Winograd in the 1970s. Winograd developed a system called SHRDLU that could answer questions about a simple block world. SHRDLU was able to do this by using a set of prompts that were designed to help it understand the context of the question.

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In the 1980s, prompt engineering became more sophisticated as researchers developed new techniques for training LLMs. One of the most important techniques was backpropagation, which allowed Large Language Models to learn from their mistakes. This made it possible to train LLMs on much larger datasets, leading to significant performance improvements.

In the 2010s, the development of deep learning led to a new wave of progress in prompt engineering. Deep learning models are able to learn much more complex relationships between words than previous models. This has made it possible to create prompts that are much more effective at controlling the output of LLMs.

Today, prompt engineering is a critical tool for researchers and developers who are working with LLMs. It is used in a wide variety of applications, including machine translation, text summarization, and creative writing.

Myths vs facts in prompt engineering

Have you tried any of these fun prompts?

  • In the field of machine translation, one researcher tried to get an LLM to translate the phrase “I am a large language model” into French. The LLM responded with “Je suis un grand modèle linguistique”, which is a grammatically correct translation, but it also happens to be the name of a popular French cheese.
  • In the field of text summarization, one researcher tried to get an LLM to summarize the plot of the movie “The Shawshank Redemption”. The LLM responded with a summary that was surprisingly accurate, but it also included a number of jokes and puns.
  • In the field of creative writing, one researcher tried to get an LLM to write a poem about a cat. The LLM responded with a poem that was both funny and touching.

These are just a few examples of the many funny prompts that people have tried with LLMs. As LLMs become more powerful, it is likely that we will see even more creative and entertaining uses of prompt engineering.

Want to improve your prompting skills? Click below:

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Some unknown facts about Prompt Engineering

  • It is a relatively new field, and there is still much that we do not know about it. However, it is a rapidly growing field, and there are many exciting new developments happening all the time.
  • The effectiveness of a prompt can depend on a number of factors, including the specific LLM being used, the training data that the LLM has been trained in, and the context in which the prompt is being used.
  • There are a number of different techniques that can be used for prompt engineering, and the best technique to use will depend on the specific application.
  • It can be used to control a wide variety of aspects of the output of an LLM, including the length, style, and content of the output.
  • It can be used to generate creative and interesting text, as well as to solve complex problems.
  • It is a powerful tool that can be used to unlock the full potential of LLMs.

 

Learn how to become a prompt engineer in 10 steps 

10 steps to become a prompt engineer
10 steps to become a prompt engineer

Here are some specific examples of important and unknown facts about prompting:

  • It is possible to use prompts to control the creativity of an LLM. For example, one study found that adding the phrase “in a creative way” to a prompt led to more creative outputs from the LLM.
  • Prompts can be used to generate text that is consistent with a particular style. For example, one study found that adding the phrase “in the style of Shakespeare” to a prompt led to outputs that were more Shakespearean in style.
  • Prompts can be used to solve complex problems. For example, one study found that adding the phrase “prove that” to a prompt led to the LLM generating mathematical proofs.
  • It is a complex and challenging task. There is no one-size-fits-all approach to prompt engineering, and the best way to create effective prompts will vary depending on the specific application.
  • It is a rapidly evolving field. There are new developments happening all the time, and the field is constantly growing and changing.

Most popular myths and facts of prompt engineering

In this ever-evolving realm, it’s crucial to discern fact from fiction to stay ahead of the curve. Our team of experts has meticulously sifted through the noise to present you with the most accurate insights, dispelling myths that might have clouded your understanding. Let’s delve into the heart of prompting and uncover the truths that can drive your success.

Myth: Prompt engineering is just about keywords

Fact: Prompt engineering is a symphony of elements

Gone are the days when prompt engineering was solely about sprinkling keywords like confetti. Today, it’s a meticulous symphony of various components working harmoniously. While keywords remain pivotal, they’re just one part of the grand orchestra. Structured data, user intent analysis, and contextual relevance are the unsung heroes that make your prompt engineering soar. Balancing these elements crafts a narrative that resonates with both users and search engines.

Myth: More prompts, higher results

Fact: Quality over quantity

Quantity might impress at first glance, but it’s quality that truly wields power in the world of prompt engineering. Crafting a handful of compelling, highly relevant prompts that align seamlessly with your content yields far superior results than flooding your page with irrelevant ones. Remember, it’s the value you provide that keeps users engaged, not the sheer number of prompts you throw their way.

Myth: Prompt engineering is a one-time task

Fact: Ongoing optimization is the key

Imagine your website as a garden that requires constant tending. Similarly, prompt engineering demands continuous attention. Regularly analyzing the performance of your prompts and adapting to shifting trends is paramount. This ensures that your content remains evergreen and resonates with the dynamic preferences of your audience.

Myth: Creativity has no place in prompt engineering

Fact: Creativity elevates engagement

While prompt engineering involves a systematic approach, creativity is the secret ingredient that adds flavor to the mix. Crafting prompts that spark curiosity, evoke emotion, or present a unique perspective can exponentially boost user engagement. Metaphors, analogies, and storytelling are potent tools that, when woven into your prompts, make your content unforgettable.

Myth: Only text prompts matter

Fact: Diversify with various formats

Text prompts are undeniably significant, but limiting yourself to them is a missed opportunity. Embrace a diverse range of prompt formats to cater to different learning styles and preferences.

Visual prompts, such as infographics and videos, engage visual learners, while audio prompts cater to those who prefer auditory learning. The more versatile your prompt formats, the broader your audience reaches.

Myth: Prompt engineering and SEO are unrelated

Fact: Symbiotic relationship

Prompt engineering and SEO are not isolated islands; they’re interconnected domains that thrive on collaboration. Solid prompt engineering bolsters SEO by providing search engines with the context they crave. Conversely, a well-optimized website enhances prompt engineering, as it ensures your content is easily discoverable by your target audience.

Myth: Complex language boosts credibility

Fact: Clarity trumps complexity

Using complex jargon might seem like a credibility booster, but it often does more harm than good. Clear, concise prompts that resonate with a broader audience hold more weight. Remember, the goal is not to showcase your vocabulary prowess but to communicate effectively and establish a genuine connection with your readers.

Myth: Prompt engineering is set-and-forget

Fact: Continuous monitoring is vital

Once you’ve orchestrated your prompts, it’s not time to sit back and relax. The digital landscape is in perpetual motion, and so should be your approach to prompt engineering. Monitor the performance of your prompts regularly, employing data analytics to identify patterns and make informed adjustments that keep your content relevant and engaging.

Myth: Only experts can master prompt engineering

Fact: Learning and iteration lead to mastery

While prompt engineering might appear daunting, it’s a skill that can be honed with dedication and a willingness to learn. Don’t shy away from experimentation and iteration. Embrace the insights gained from your data, be open to refining your approach, and gradually you’ll find yourself mastering the art of prompt engineering.

Get on the journey of prompt engineering

Prompt engineering is a dynamic discipline that demands both strategy and creativity. Dispelling these myths and embracing the facts will propel your content to new heights, setting you apart from the competition. Remember, prompt engineering is not a one-size-fits-all solution; it’s an evolving journey of discovery that, when approached with dedication and insight, can yield remarkable results

August 21, 2023

Large language models (LLMs) are a type of artificial intelligence (AI) that are trained on a massive dataset of text and code. Learn LLMs to generate text, translate languages, write different kinds of creative content, and answer your questions in an informative way.

Before we dive into the impact Large Language Models will create on different areas of work, let’s test your knowledge in the domain. Take our quiz for your current understanding and learn LLMs further.

Are You Ready to Test Your Knowledge? Learn LLMs and Put it to Test with our Quiz | Data Science Dojo

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Why are LLMs the Next Big Thing to Learn About?

Knowing about LLMs can be important for scaling your career in a number of ways.

 

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  • LLMs are becoming increasingly powerful and sophisticated. As LLMs become more powerful and sophisticated, they are being used in a variety of applications, such as machine translation, chatbots, and creative writing. This means that there is a growing demand for people who understand how to use LLMs effectively.
  • Prompt engineering is a valuable skill that can be used to improve the performance of LLMs in a variety of tasks. By understanding how to engineer prompts, you can get the most out of LLMs and use them to accomplish a variety of tasks. This is a valuable skill that can be used to improve the performance of LLMs in a variety of tasks.
  • Learning about LLMs and prompt engineering can help you to stay ahead of the curve in the field of AI. As LLMs become more powerful and sophisticated, they will have a significant impact on a variety of industries. By understanding how LLMs work, you will be better prepared to take advantage of this technology in the future.

Here are some specific examples of how knowing about LLMs can help you to scale your career:

  • If you are a software engineer, you can use LLMs to automate tasks, such as code generation and testing. This can free up your time to focus on more strategic work.
  • If you are a data scientist, you can use LLMs to analyze large datasets and extract insights. This can help you to make better decisions and improve your business performance.
  • If you are a marketer, you can use LLMs to create personalized content and generate leads. This can help you to reach your target audience and grow your business.

Overall, knowing about LLMs can be a valuable asset for anyone who is looking to scale their career. By understanding how LLMs work and how to use them effectively, you can become a more valuable asset to your team and your company.

Here are some additional reasons why knowing about LLMs can be important for scaling your career:

  • LLMs are becoming increasingly popular. As LLMs become more popular, there will be a growing demand for people who understand how to use them effectively. This means that there will be more opportunities for people who have knowledge of LLMs.
  • LLMs are a rapidly developing field. The field of LLMs is constantly evolving, and there are new developments happening all the time. This means that there is always something new to learn about LLMs, which can help you to stay ahead of the curve in your career.
  • LLMs are a powerful tool that can be used to solve a variety of problems. LLMs can be used to solve a variety of problems, from machine translation to creative writing. This means that there are many different ways that you can use your knowledge of LLMs to make a positive impact in the world.

 

Read more about —->> How to deploy custom LLM applications for your business 

August 1, 2023

In today’s era of advanced artificial intelligence, language models like OpenAI’s GPT-3.5 have captured the world’s attention with their astonishing ability to generate human-like text. However, to harness the true potential of these models, it is crucial to master the art of prompt engineering strategies.

How to Curate a Good Prompt?

A well-crafted prompt holds the key to unlocking accurate, relevant, and insightful responses from language models. In this blog post, we will explore the top characteristics of a good prompt and discuss why everyone should learn prompt engineering. We will also delve into the question of whether prompt engineering might emerge as a dedicated role in the future.

 

Best practices for prompt engineering

 

Prompt engineering refers to the process of designing and refining input prompts for AI language models to produce desired outputs. It involves carefully crafting the words, phrases, symbols, and formats used as input to guide the model in generating accurate and relevant responses. The goal of prompt engineering is to improve the performance and output quality of the language model.

Here’s a Simple Example to Illustrate Prompt Engineering:

Imagine you are using a chatbot AI model to provide information about the weather. Instead of a generic prompt like “What’s the weather like?”, prompt engineering involves crafting a more specific and detailed prompt like “What is the current temperature in New York City?” or “Will it rain in London tomorrow?”

 

Read about —> Which AI chatbot is right for you?

 

By providing a clear and specific prompt, you guide the AI model to generate a response that directly answers your question. The choice of words, context, and additional details in the prompt can influence the output of the AI model and ensure it produces accurate and relevant information.

 

Quick exercise –> Choose the most suitable prompt

 

Prompt engineering is crucial because it helps optimize the performance of AI models by tailoring the input prompts to the desired outcomes. It requires creativity, understanding of the language model, and attention to detail to strike the right balance between specificity and relevance in the prompts.

Different resources provide guidance on best practices and techniques for prompt engineering, considering factors like prompt formats, context, length, style, and desired output. Some platforms, such as OpenAI API, offer specific recommendations and examples for effective prompt engineering.

Why Everyone Should Learn About Prompt Engineering Strategies

 

Prompt engineering - Marketoonist
Prompt Engineering | Credits: Marketoonist

 

1. Empowering communication: Effective communication is at the heart of every interaction. By mastering prompt engineering, individuals can enhance their ability to extract precise and informative responses from language models. Whether you are a student, professional, researcher, or simply someone seeking knowledge, prompt engineering equips you with a valuable tool to engage with AI systems more effectively.

2. Tailored and relevant information: A well-designed prompt allows you to guide the language model towards providing tailored and relevant information. By incorporating specific details and instructions, you can ensure that the generated responses align with your desired goals. Prompt engineering enables you to extract the exact information you seek, saving time and effort in sifting through irrelevant or inaccurate results.

3. Enhancing critical thinking: Crafting prompts demand careful consideration of context, clarity, and open-endedness. Engaging in prompt engineering exercises cultivates critical thinking skills by challenging individuals to think deeply about the subject matter, formulate precise questions, and explore different facets of a topic. It encourages creativity and fosters a deeper understanding of the underlying concepts.

4. Overcoming bias: Bias is a critical concern in AI systems. By learning prompt engineering, individuals can contribute to reducing bias in generated responses. Crafting neutral and unbiased prompts helps prevent the introduction of subjective or prejudiced language, resulting in more objective and balanced outcomes.

Top Characteristics of a Good Prompt With Examples

 

prompt engineering strategies
An example of a good prompt – Credits Gridfiti

 

A good prompt possesses several key characteristics that can enhance the effectiveness and quality of the responses generated. Here are the top characteristics of a good prompt:

1. Clarity:

A good prompt should be clear and concise, ensuring that the desired question or topic is easily understood. Ambiguous or vague prompts can lead to confusion and produce irrelevant or inaccurate responses.

Example:

Good Prompt: “Explain the various ways in which climate change affects the environment.”

Poor Prompt: “Climate change and the environment.”

2. Specificity:

Providing specific details or instructions in a prompt help focus the generated response. By specifying the context, parameters, or desired outcome, you can guide the language model to produce more relevant and tailored answers.

Example:

Good Prompt: “Provide three examples of how rising temperatures due to climate change impact marine ecosystems.”
Poor Prompt: “Talk about climate change.”

3. Context:

Including relevant background information or context in the prompt helps the language model understand the specific domain or subject matter. Contextual cues can improve the accuracy and depth of the generated response.

Example: 

Good Prompt: “In the context of agricultural practices, discuss how climate change affects crop yields.”

Poor Prompt: “Climate change effects

4. Open Endedness:

While specificity is important, an excessively narrow prompt may limit the creativity and breadth of the generated response. Allowing room for interpretation and open-ended exploration can lead to more interesting and diverse answers.

Example:

Good Prompt: “Describe the short-term and long-term consequences of climate change on global biodiversity.”

Poor Prompt: “List the effects of climate change.”

 

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5. Conciseness:

Keeping the prompt concise helps ensure that the language model understands the essential elements and avoids unnecessary distractions. Lengthy or convoluted prompts might confuse the model and result in less coherent or relevant responses.

Example:
Good Prompt: “Summarize the key impacts of climate change on coastal communities.”

Poor Prompt: “Please explain the negative effects of climate change on the environment and people living near the coast.”

6. Correct Grammar and Syntax:

A well-structured prompt with proper grammar and syntax is easier for the language model to interpret accurately. It reduces ambiguity and improves the chances of generating coherent and well-formed responses.

Example:

Good Prompt: “Write a paragraph explaining the relationship between climate change and species extinction.”
Poor Prompt: “How species extinction climate change.”

7. Balanced Complexity:

The complexity of the prompt should be appropriate for the intended task or the model’s capabilities. Extremely complex prompts may overwhelm the model, while overly simplistic prompts may not challenge it enough to produce insightful or valuable responses.

Example:

Good Prompt: “Discuss the interplay between climate change, extreme weather events, and natural disasters.”

Poor Prompt: “Climate change and weather.”

8. Diversity in Phrasing:

When exploring a topic or generating multiple responses, varying the phrasing or wording of the prompt can yield diverse perspectives and insights. This prevents the model from repeating similar answers and encourages creative thinking.

Example:

Good Prompt: “How does climate change influence freshwater availability?” vs. “Explain the connection between climate change and water scarcity.”

Poor Prompt: “Climate change and water.

9. Avoiding Leading or Biased Language:

To promote neutrality and unbiased responses, it’s important to avoid leading or biased language in the prompt. Using neutral and objective wording allows the language model to generate more impartial and balanced answers.

Example:

Good Prompt: “What are the potential environmental consequences of climate change?”

Poor Prompt: “How does climate change devastate the environment?”

10. Iterative Refinement:

Crafting a good prompt often involves an iterative process. Reviewing and refining the prompt based on the generated responses can help identify areas of improvement, clarify instructions, or address any shortcomings in the initial prompt.

Example:

Prompt iteration involves an ongoing process of improvement based on previous responses and refining the prompts accordingly. Therefore, there is no specific example to provide, as it is a continuous effort.

By considering these characteristics, you can create prompts that elicit meaningful, accurate, and relevant responses from the language model.

 

Read about —-> How LLMs (Large Language Models) technology is making chatbots smarter?

 

Two Different Approaches of Prompting

Prompting by instruction and prompting by example are two different approaches to guide AI language models in generating desired outputs. Here’s a detailed comparison of both approaches, including reasons and situations where each approach is suitable:

1. Prompting by Instruction:

  • In this approach, the prompt includes explicit instructions or explicit questions that guide the AI model on how to generate the desired output.
  • It is useful when you need specific control over the generated response or when you want the model to follow a specific format or structure.
  • For example, if you want the AI model to summarize a piece of text, you can provide an explicit instruction like “Summarize the following article in three sentences.”
  • Prompting by instruction is suitable when you need a precise and specific response that adheres to a particular requirement or when you want to enforce a specific behavior in the model.
  • It provides clear guidance to the model and allows you to specify the desired outcome, length, format, style, and other specific requirements.

 

How generative AI and LLMs work

 

Examples of prompting by instruction:

  1. In a classroom setting, a teacher gives explicit verbal instructions to students on how to approach a new task or situation, such as explaining the steps to solve a math problem.
  2. In Applied Behavior Analysis (ABA), a therapist provides a partial physical prompt by using their hands to guide a student’s behavior in the right direction when teaching a new skill.
  3. When using AI language models, an explicit instruction prompt can be given to guide the model’s behavior. For example, providing the instruction “Summarize the following article in three sentences” to prompt the model to generate a concise summary.

Tips for prompting by instruction:

    • Put the instructions at the beginning of the prompt and use clear markers like “A:” to separate instructions and context.
    • Be specific, descriptive, and detailed about the desired context, outcome, format, style, etc.
    • Articulate the desired output format through examples, providing clear guidelines for the model to follow.

2. Prompting by Example:

  • In this approach, the prompt includes examples of the desired output or similar responses that guide the AI model to generate responses based on those examples.
  • It is useful when you want the model to learn from specific examples and mimic the desired behavior.
  • For example, if you want the AI model to answer questions about a specific topic, you can provide example questions and their corresponding answers.
  • Prompting by example is suitable when you want the model to generate responses similar to the provided examples or when you want to capture the style, tone, or specific patterns from the examples.
  • It allows the model to learn from the given examples and generalize its behavior based on them.

Examples of prompting by example:

  1. In a classroom, a teacher shows students a model essay as an example of how to structure and write their own essays, allowing them to learn from the demonstrated example.
  2. In AI language models, providing example questions and their corresponding answers can guide the model in generating responses similar to the provided examples. This helps the model learn the desired behavior and generalize it to new questions.
  3. In an online learning environment, an instructor provides instructional prompts in response to students’ discussion forum posts, guiding the discussion and encouraging deep understanding. These prompts serve as examples for the entire class to enhance the learning experience.

Tips for prompting by example:

    • Provide a variety of examples to capture different aspects of the desired behavior.
    • Include both positive and negative examples to guide the model on what to do and what not to do.
    • Gradually refine the examples based on the model’s responses, iteratively improving the desired behavior.

 

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Which Prompting Approach is Right For You?

Prompting by instruction provides explicit guidance and control over the model’s behavior, while prompting by example allows the model to learn from provided examples and mimic the desired behavior. The choice between the two approaches depends on the level of control and specificity required for the task at hand. It’s also possible to combine both approaches in a single prompt to leverage the benefits of each approach for different parts of the task or desired behavior.

 

To become proficient in prompt engineering, register now in our upcoming Large Language Models Bootcamp

July 12, 2023

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