The evolution of large language models (LLMs) has revolutionized many fields, including analytics. Traditionally, LLMs have been integrated into analytics workflows to assist in explaining data, generating summaries, and uncovering insights. However, a more recent breakthrough in AI, Agentic AI which involves the development of AI systems, composed of multiple agents, each with a defined purpose, capable of autonomous decision-making and self-directed actions.
This shift is now making its way into the analytics domain, transforming how we interact with data. According to Gartner:
By 2026, over 80% of business consumers will prefer intelligent assistants and embedded analytics over dashboards for data-driven insights.
Agentic AI is reshaping the analytics landscape by enabling conversational, intelligent, and proactive data experiences.
In this blog, we’ll explore how agentic analytics is redefining data workflows and making data-driven decision-making more accessible, intelligent, and efficient.
What is Agentic Analytics?
In the realm of data analytics, driving insights is often a complex and time-consuming process. Data professionals invest significant effort in preparing the right data, cleaning and organizing it, and finally reaching meaningful conclusions. With the rise of LLM-powered agents, many of these tasks have become easier and more efficient.
Today, different types of agents can be employed at various stages of the analytics lifecycle. When these agents are granted autonomy and integrated across the entire analytics workflow, they form a cohesive, intelligent system known as Agentic Analytics. This paradigm shift enables more conversational, dynamic, and accessible ways to work with data.
Why Shift to Agentic Analytics?
How Does Agentic Analytics Differ?
To better understand the impact of Agentic Analytics, let’s compare it with traditional business intelligence approaches and AI-assisted methods:
How It Works: Components of Agentic Analytics
Agentic Analytics brings together Agentic AI and data analytics to turn raw business data into intelligent, actionable insights. To achieve this, it builds on the core architectural components of Agentic AI, enhanced with analytics-specific modules. Let’s break down some key components:
1. AI Agents (LLM-Powered)
At the core of Agent Analytics are autonomous AI agents, powered by large language models (LLMs). These agents can:
Access and query data sources
Interpret user intent
Generate automated insights and summaries
Take actions like triggering alerts, or recommending decisions
2. Memory and Learning Module
This component stores user preferences, like frequently asked questions, preferred data formats, past interactions, and recurring topics. By leveraging this memory, the system personalizes future responses and learns over time, leading to smarter, more relevant interactions.
3. Semantic Module
The semantic layer is foundational to both analytics and agentic AI. It serves as a unified interface that bridges the gap between raw data and business context, adding business logic, key metrics, governance, and consistency to raw data, ensuring that insights are not only accurate but also aligned with the organization’s definitions and standards.
4. Data Sources & Tools Integration
Agentic Analytics systems must connect to a wide variety of data sources and tools that agents can access to perform their tasks. These include structured databases, analytics tools, ETL tools, business applications, etc.
Agentic Analytics systems are powered by a collection of specialized autonomous agents, each with a clear role in the analytics lifecycle. Let’s have a look at some fundamental agents involved in analytics:
1. Planner Agent
Acts as the strategist. Breaks down a business request into smaller analytical tasks, assigns them to the right agents, and manages the execution to ensure goals are met efficiently.
Example:
A business launched a new smartwatch, and now the project manager needs a report to “assess sales, engagement, and market reception.” The Planner Agent interprets the goal and creates a multi-step workflow and delegates tasks to the appropriate Agents.
2. Data Agent
Acts as the data connector. Identifies the right data sources, retrieves relevant datasets, and ensures secure, accurate access to information across internal systems and external APIs.
Example:
The Data Agent pulls sales data from the ERP, website analytics from Google Analytics, customer reviews from e-commerce platforms, and social media mentions via APIs.
3. Data Preparation Agent
Acts as the data wrangler. Cleans, transforms, and enriches datasets so they are ready for analysis. Handles formatting, joins, missing values, and data consistency checks.
The Prep Agent merges sales and marketing data, enriches customer profiles with demographic details, and prepares engagement metrics for further analysis.
4. Analysis Agent
Acts as the analyst. Selects and applies the appropriate analytical or statistical methods to uncover patterns, trends, and correlations in the data by generating code or SQL queries.
Example:
The Analysis Agent calculates units sold per region, tracks repeat purchase rates, compares previous launch sales with new ones, identifies the most effective marketing campaigns, and detects patterns.
5. Visualization Agent
Acts as the storyteller. Generates visuals, charts, and tables that make complex data easy to understand for different stakeholders.
Example:
The Visualization Agent builds interactive dashboards showing sales heatmaps, engagement trends over time, and customer sentiment charts.
6. Monitoring Agent
Acts as the supervisor. Monitors results from all agents and ensures actions are initiated when needed.
Example:
The agent coordinates with other agents, monitors sales, and sets up real-time alerts for sentiment drops or sales spikes.
Real-World Examples of Agentic Analytics Platforms
Tableau Next
Tableau Next is Salesforce’s next-generation agentic analytics platform, tightly integrated with Agentforce, Salesforce’s digital labor framework. Its data foundation ensures enterprise-grade security, compliance, and agility while unifying customer data for holistic analysis.
Built as an open, API-first platform, Tableau Next offers reusable, discoverable analytic assets and a flexible architecture to meet evolving business needs. By embedding AI-powered insights directly into workflows, it allows decision-makers to act on relevant, real-time intelligence without switching tools, making insight delivery truly seamless.
source: Tableau
ThoughtSpot
ThoughtSpot delivers fast, accurate AI-driven insights through a unified platform powered by AI agents, connected insights, and smart applications. It streamlines the entire analytics lifecycle from data connection, exploration, and action into a single, cohesive environment.
Unlike traditional BI tools that require users to log into dashboards and search for answers, it allows organizations to integrate analytics into custom apps and workflows effortlessly. Every AI-generated insight is fully transparent, with the ability to verify results through natural language tokens or SQL queries, ensuring trust, governance, and AI explainability.
source: Thoughtspot
Tellius
Tellius combines dynamicAI agents with conversational intelligence to make analytics accessible to everyone.
The platform integrates data from multiple systems into a secure, unified knowledge layer, eliminating silos and creating a single source of truth. Multi-agent workflows handle tasks such as planning, data preparation, insight generation, and visualization. These agents operate proactively, delivering anomaly detection, segmentation, root-cause analysis, and actionable recommendations in real time.
While agentic analytics offers tremendous potential, realizing its benefits requires addressing several practical and strategic challenges:
Data Quality and Integration
Even the most sophisticated AI agents are limited by the quality of the data they consume. Siloed, inconsistent, or incomplete data can severely degrade output accuracy. To mitigate this, organizations should prioritize integrating curated datasets and implementing a semantic layer, offering a unified and consolidated view across the organization.
Cost Management
Autonomous AI agents often operate in a continuous listening mode, constantly ingesting data and running analysis, causing high token consumption and operational cost. Techniques like Agentic Retrieval-Augmented Generation (RAG) and context filtering can reduce unnecessary data queries and optimize cost efficiency.
Trust and Transparency
Building trust, transparency, and explainability in agentic systems becomes fundamental as users are made to rely on AI-driven decisions. Incorporating transparent decision logs, natural language explanations and clear traceability back to source data and agentic flow help users not only verify results but also understand the process of their generation.
Security and Compliance
When AI agents are given autonomy to pull, process, and act on enterprise data, strict access control and compliance safeguards are essential. This includes role-based data access, data masking for sensitive fields, and audit trails for agent actions. It also involves ensuring agent operations align with industry-specific regulations such as GDPR or HIPAA.
Response Quality
AI agents have the tendency to produce responses that differ from business logic, raising concerns about their use in decision-making. To address this, a clear orchestration framework with well-defined agents is essential. Other strategies include adding a semantic layer for consistent business definitions and a reinforcement learning layer to enable learning from past feedback.
Agentic analytics represents an evolution in the analytics landscape where insights are no longer just discovered but are contextual, conversational, and actionable. With Agentic AI, insights are described, root cause is diagnosed, outcomes are predicted, and corrective actions are prescribed, all autonomously.
To unlock this potential, organizations must implement an agentic system, ensuring transparency, maintaining security and governance, aligning with business requirements, and leveraging curated, trusted data.
According to Gartner, augmented analytics capabilities will evolve into autonomous analytics platforms by 2027, with 75% of analytics content leveraging GenAI for enhanced contextual intelligence. Organizations must prepare today to lead tomorrow, harnessing what, why, and how of data in a fully automated, intelligent way.
Byte pair encoding (BPE) has quietly become one of the most influential algorithms in natural language processing (NLP) and machine learning. If you’ve ever wondered how models like GPT, BERT, or Llama handle vast vocabularies and rare words, the answer often lies in byte pair encoding. In this comprehensive guide, we’ll demystify byte pair encoding, explore its origins, applications, and impact on modern AI, and show you how to leverage BPE in your own data science projects.
What is Byte Pair Encoding?
Byte pair encoding is a data compression and tokenization algorithm that iteratively replaces the most frequent pair of bytes (or characters) in a sequence with a new, unused byte. Originally developed for data compression, BPE has found new life in NLP as a powerful subword segmentation technique.
Traditional tokenization methods, splitting text into words or characters, struggle with rare words, misspellings, and out-of-vocabulary (OOV) terms. BPE bridges the gap by breaking words into subword units, enabling models to handle any input text, no matter how unusual.
The Origins of Byte Pair Encoding
BPE was first introduced by Philip Gage in 1994 as a simple data compression algorithm. Its core idea was to iteratively replace the most common pair of adjacent bytes in a file with a byte that does not occur in the file, thus reducing file size.
In 2015, Sennrich, Haddow, and Birch adapted BPE for NLP, using it to segment words into subword units for neural machine translation. This innovation allowed translation models to handle rare and compound words more effectively.
Byte Pair Encoding (BPE) is a powerful algorithm for tokenizing text, especially in natural language processing (NLP). Its strength lies in transforming raw text into manageable subword units, which helps language models handle rare words and diverse vocabularies. Let’s walk through the BPE process in detail:
1. Initialize the Vocabulary
Context:
The first step in BPE is to break down your entire text corpus into its smallest building blocks, individual characters. This granular approach ensures that every possible word, even those not seen during training, can be represented using the available vocabulary.
Process:
List every unique character found in your dataset (e.g., a-z, punctuation, spaces).
For each word, split it into its constituent characters.
Append a special end-of-word marker (eg “</w>” or “▁”) to each word. This marker helps the algorithm distinguish between words and prevents merges across word boundaries.
Example:
Suppose your dataset contains the words:
“lower” → l o w e r</w>
“lowest” → l o w e s t</w>
“newest” → n e w e s t</w>
Why the end-of-word marker?
It ensures that merges only happen within words, not across them, preserving word boundaries and meaning.
Now, the algorithm looks for patterns specifically, pairs of adjacent symbols (characters or previously merged subwords) within each word. By counting how often each pair appears, BPE identifies which combinations are most common and thus most useful to merge.
Process:
For every word, list all adjacent symbol pairs.
Tally the frequency of each pair across the entire dataset.
Example:
For “lower” (l o w e r ), the pairs are:
(l, o), (o, w), (w, e), (e, r), (r, )
For “lowest” (l o w e s t ):
(l, o), (o, w), (w, e), (e, s), (s, t), (t, )
For “newest” (n e w e s t ):
(n, e), (e, w), (w, e), (e, s), (s, t), (t, )
Frequency Table Example:
3. Merge the Most Frequent Pair
Context:
The heart of BPE is merging. By combining the most frequent pair into a new symbol, the algorithm creates subword units that capture common patterns in the language.
Process:
Identify the pair with the highest frequency.
Merge this pair everywhere it appears in the dataset, treating it as a single symbol in future iterations.
Example:
Suppose (w, e) is the most frequent pair (appearing 3 times).
Merge “w e” into “we”.
Update the words:
“lower” → l o we r
“lowest” → l o we s t
“newest” → n e we s t
Note:
After each merge, the vocabulary grows to include the new subword (“we” in this case).
BPE is an iterative algorithm. After each merge, the dataset changes, and new frequent pairs may emerge. The process continues until a stopping criterion is met, usually a target vocabulary size or a set number of merges.
Process:
Recount all adjacent symbol pairs in the updated dataset.
Merge the next most frequent pair.
Update all words accordingly.
Example:
If (o, we) is now the most frequent pair, merge it to “owe”:
“lower” → l owe r
“lowest” → l owe s t
Continue merging:
“lower” → low er
“lowest” → low est
“newest” → new est
Iteration Table Example:
5. Build the Final Vocabulary
Context:
After the desired number of merges, the vocabulary contains both individual characters and frequently occurring subword units. This vocabulary is used to tokenize any input text, allowing the model to represent rare or unseen words as sequences of known subwords.
Process:
The final vocabulary includes all original characters plus all merged subwords.
Any word can be broken down into a sequence of these subwords, ensuring robust handling of out-of-vocabulary terms.
Example:
Final vocabulary might include:
{l, o, w, e, r, s, t, n, we, owe, low, est, new, lower, lowest, newest, }
Tokenization Example:
“lower” → lower
“lowest” → low est
“newest” → new est
Why Byte Pair Encoding Matters in NLP
Handling Out-of-Vocabulary Words
Traditional word-level tokenization fails when encountering new or rare words. BPE’s subword approach ensures that any word, no matter how rare, can be represented as a sequence of known subwords.
Efficient Vocabulary Size
BPE allows you to control the vocabulary size, balancing model complexity and coverage. This is crucial for deploying models on resource-constrained devices or scaling up to massive datasets.
Improved Generalization
By breaking words into meaningful subword units, BPE enables models to generalize better across languages, dialects, and domains.
Byte Pair Encoding in Modern Language Models
BPE is the backbone of tokenization in many state-of-the-art language models:
GPT & GPT-2/3/4: Use BPE to tokenize input text, enabling efficient handling of diverse vocabularies.
BERT & RoBERTa: Employ similar subword tokenization strategies (WordPiece, SentencePiece) inspired by BPE.
Llama, Qwen, and other transformer models: Rely on BPE or its variants for robust, multilingual tokenization.
Practical Applications of Byte Pair Encoding
1. Machine Translation
BPE enables translation models to handle rare words, compound nouns, and morphologically rich languages by breaking them into manageable subwords.
2. Text Generation
Language models use BPE to generate coherent text, even when inventing new words or handling typos.
3. Data Compression
BPE’s roots in data compression make it useful for reducing the size of text data, especially in resource-limited environments.
4. Preprocessing for Neural Networks
BPE simplifies text preprocessing, ensuring consistent tokenization across training and inference.
Implementing Byte Pair Encoding: A Hands-On Example
Let’s walk through a simple Python implementation using the popular tokenizers library from Hugging Face:
This code trains a custom Byte Pair Encoding (BPE) tokenizer using the Hugging Face tokenizers library. It first initializes a BPE model and applies a whitespace pre-tokenizer so that words are split on spaces before subword merges are learned. A BpeTraineris then configured with a target vocabulary size of 10,000 tokens and a minimum frequency threshold, ensuring that only subwords appearing at least twice are included in the final vocabulary. The tokenizer is trained on a text corpus your_corpus.text (you may use whatever text you want to tokenize here), during which it builds a vocabulary and set of merge rules based on the most frequent character pairs in the data. Once trained, the tokenizer can encode new text by breaking it into tokens (subwords) according to the learned rules, which helps represent both common and rare words efficiently.
Byte Pair Encoding vs. Other Tokenization Methods
Challenges and Limitations
Morpheme Boundaries: BPE merges based on frequency, not linguistic meaning, so subwords may not align with true morphemes.
Language-Specific Issues: Some languages (e.g., Chinese, Japanese) require adaptations for optimal performance.
Vocabulary Tuning: Choosing the right vocabulary size is crucial for balancing efficiency and coverage.
Start with 10,000–50,000 tokens for most NLP tasks; adjust based on dataset and model size.
Preprocess Consistently:
Ensure the same BPE vocabulary is used during training and inference.
Monitor OOV Rates:
Analyze how often your model encounters unknown tokens and adjust accordingly.
Combine with Other Techniques:
For multilingual or domain-specific tasks, consider hybrid approaches (e.g., SentencePiece, Unigram LM).
Real-World Example: BPE in GPT-3
OpenAI’s GPT-3 uses a variant of BPE to tokenize text into 50,257 unique tokens, balancing efficiency and expressiveness. This enables GPT-3 to handle everything from code to poetry, across dozens of languages.
FAQ: Byte Pair Encoding
Q1: Is byte pair encoding the same as WordPiece or SentencePiece?
A: No, but they are closely related. WordPiece and SentencePiece are subword tokenization algorithms inspired by BPE, each with unique features.
Q2: How do I choose the right vocabulary size for BPE?
A: It depends on your dataset and model. Start with 10,000–50,000 tokens and experiment to find the sweet spot.
Q3: Can BPE handle non-English languages?
A: Yes! BPE is language-agnostic and works well for multilingual and morphologically rich languages.
Q4: Is BPE only for NLP?
A: While most popular in NLP, BPE’s principles apply to any sequential data, including DNA sequences and code.
Conclusion: Why Byte Pair Encoding Matters for Data Scientists
Byte pair encoding is more than just a clever algorithm, it’s a foundational tool that powers the world’s most advanced language models. By mastering BPE, you’ll unlock new possibilities in NLP, machine translation, and AI-driven applications. Whether you’re building your own transformer model or fine-tuning a chatbot, understanding byte pair encoding will give you a competitive edge in the fast-evolving field of data science.
Qwen models have rapidly become a cornerstone in the open-source large language model (LLM) ecosystem. Developed by Alibaba Cloud, these models have evolved from robust, multilingual LLMs to the latest Qwen 3 series, which sets new standards in reasoning, efficiency, and agentic capabilities. Whether you’re a data scientist, ML engineer, or AI enthusiast, understanding the Qwen models, especially the advancements in Qwen 3, will empower you to build smarter, more scalable AI solutions.
In this guide, we’ll cover the full Qwen model lineage, highlight the technical breakthroughs of Qwen 3, and provide actionable insights for deploying and fine-tuning these models in real-world applications.
source: inferless
What Are Qwen Models?
Qwen models are a family of open-source large language models developed by Alibaba Cloud. Since their debut, they have expanded into a suite of LLMs covering general-purpose language understanding, code generation, math reasoning, vision-language tasks, and more. Qwen models are known for:
Multilingual support(now up to 119 languages in Qwen 3).
Open-source licensing(Apache 2.0), making them accessible for research and commercial use.
Specialized variants for coding (Qwen-Coder), math (Qwen-Math), and multimodal tasks (Qwen-VL).
Why Qwen Models Matter:
They offer a unique blend of performance, flexibility, and openness, making them ideal for both enterprise and research applications. Their rapid evolution has kept them at the cutting edge of LLM development.
The Evolution of Qwen: From Qwen 1 to Qwen 3
Qwen 1 & Qwen 1.5
Initial releases focused on robust transformer architectures and multilingual capabilities.
Context windows up to 32K tokens.
Strong performance in Chinese and English, with growing support for other languages.
Qwen 2 & Qwen 2.5
Expanded parameter sizes (up to 110B dense, 72B instruct).
Improved training data (up to 18 trillion tokens in Qwen 2.5).
Enhanced alignment via supervised fine-tuning and Direct Preference Optimization (DPO).
Specialized models for math, coding, and vision-language tasks.
Qwen 3: The Breakthrough Generation
Released in 2025, Qwen 3 marks a leap in architecture, scale, and reasoning.
Model lineup includes both dense and Mixture-of-Experts (MoE) variants, from 0.6B to 235B parameters.
Hybrid reasoning modes (thinking and non-thinking) for adaptive task handling.
Multilingual fluency across 119 languages and dialects.
Agentic capabilities for tool use, memory, and autonomous workflows.
Open-weight models under Apache 2.0, available on Hugging Face and other platforms.
Qwen 3: Architecture, Features, and Advancements
Architectural Innovations
Mixture-of-Experts (MoE):
Qwen 3’s flagship models (e.g., Qwen3-235B-A22B) use MoE architecture, activating only a subset of parameters per input. This enables massive scale (235B total, 22B active) with efficient inference and training.
Bundles similar queries to reduce redundant computation, boosting throughput and lowering latency, critical for interactive and coding applications.
Global-Batch Load Balancing:
Distributes computational load evenly across experts, ensuring stable, high-throughput training even at massive scale.
Hybrid Reasoning Modes:
Qwen 3 introduces “thinking mode” (for deep, step-by-step reasoning) and “non-thinking mode” (for fast, general-purpose responses). Users can dynamically switch modes via prompt tags or API parameters.
Unified Chat/Reasoner Model:
Unlike previous generations, Qwen 3 merges instruction-following and reasoning into a single model, simplifying deployment and enabling seamless context switching.
Q3: How does Qwen 3 compare to Llama 3, DeepSeek, or GPT-4o?
A: Qwen 3 matches or exceeds these models in coding, reasoning, and multilingual tasks, with the added benefit of open-source weights and a full suite of model sizes.
Q4: What are the best resources to learn more about Qwen models?
Qwen models have redefined what’s possible in open-source large language models. With Qwen 3, Alibaba has delivered a suite of models that combine scale, efficiency, reasoning, and agentic capabilities, making them a top choice for developers, researchers, and enterprises alike.
The world of large language models (LLMs) is evolving at breakneck speed. With each new release, the bar for performance, efficiency, and accessibility is raised. Enter Deep Seek v3.1—the latest breakthrough in open-source AI that’s making waves across the data science and AI communities.
Whether you’re a developer, researcher, or enterprise leader, understanding Deep Seek v3.1 is crucial for staying ahead in the rapidly changing landscape of artificial intelligence. In this guide, we’ll break down what makes Deep Seek v3.1 unique, how it compares to other LLMs, and how you can leverage its capabilities for your projects.
Deep Seek v3.1 is an advanced, open-source large language model developed by DeepSeek AI. Building on the success of previous versions, v3.1 introduces significant improvements in reasoning, context handling, multilingual support, and agentic AI capabilities.
Key Features at a Glance
Hybrid Inference Modes:
Supports both “Think” (reasoning) and “Non-Think” (fast response) modes for flexible deployment.
Expanded Context Window:
Processes up to 128K tokens (with enterprise versions supporting up to 1 million tokens), enabling analysis of entire codebases, research papers, or lengthy legal documents.
Enhanced Reasoning:
Up to 43% improvement in multi-step reasoning over previous models.
Superior Multilingual Support:
Over 100 languages, including low-resource and Asian languages.
Reduced Hallucinations:
38% fewer hallucinations compared to earlier versions.
Open-Source Weights:
Available for research and commercial use via Hugging Face.
Agentic AI Skills:
Improved tool use, multi-step agent tasks, and API integration for building autonomous AI agents.
Deep Dive: Technical Architecture of Deep Seek v3.1
Model Structure
Parameters:
671B total, 37B activated per token (Mixture-of-Experts architecture)
Training Data:
840B tokens, with extended long-context training phases
Tokenizer:
Updated for efficiency and multilingual support
Context Window:
128K tokens (with enterprise options up to 1M tokens)
Hybrid Modes:
Switch between “Think” (deep reasoning) and “Non-Think” (fast inference) via API or UI toggle
Hybrid Inference: Think vs. Non-Think
Think Mode:
Activates advanced reasoning, multi-step planning, and agentic workflows—ideal for complex tasks like code generation, research, and scientific analysis.
Non-Think Mode:
Prioritizes speed for straightforward Q&A, chatbots, and real-time applications.
Agentic AI & Tool Use
Deep Seek v3.1 is designed for the agent era, supporting:
Strict Function Calling:
For safe, reliable API integration
Tool Use:
Enhanced post-training for multi-step agent tasks
Code & Search Agents:
Outperforms previous models on SWE/Terminal-Bench and complex search tasks
Benchmarks & Performance: How Does Deep Seek v3.1 Stack Up?
Benchmark Results
DeepSeek-V3.1 demonstrates consistently strong benchmark performance across a wide range of evaluation tasks, outperforming both DeepSeek-R1-0528 and DeepSeek-V3-0324 in nearly every category. On browsing and reasoning tasks such as Browsecomp (30.0 vs. 8.9) and xbench-DeepSearch (71.2 vs. 55.0), V3.1 shows a clear lead, while also maintaining robust results in multi-step reasoning and information retrieval benchmarks like Frames (83.7) and SimpleQA (93.4). In more technically demanding evaluations such as SWE-bench Verified (66.0) and SWE-bench Multilingual (54.5), V3.1 delivers significantly higher accuracy than its counterparts, reflecting its capability for complex software reasoning. Terminal-Bench results further reinforce this edge, with V3.1 (31.3) scoring well above both V3-0324 and R1-0528. Interestingly, while R1-0528 tends to generate longer outputs, as seen in AIME 2025, GPQA Diamond, and LiveCodeBench, V3.1-Think achieves higher efficiency with competitive coverage, producing concise yet effective responses. Overall, DeepSeek-V3.1 stands out as the most balanced and capable model, excelling in both natural language reasoning and code-intensive benchmarks.
Real-World Performance
Code Generation: Outperforms many closed-source models in code benchmarks and agentic tasks.
Multilingual Tasks: Near-native proficiency in 100+ languages.
Long-Context Reasoning: Handles entire codebases, research papers, and legal documents without losing context.
Deep Seek v3.1 is not just a technical marvel—it’s a statement for open, accessible AI. By releasing both the full and smaller (7B parameter) versions as open source, DeepSeek AI empowers researchers, startups, and enterprises to innovate without the constraints of closed ecosystems.
Q1: How does Deep Seek v3.1 compare to GPT-4 or Llama 3?
A: Deep Seek v3.1 matches or exceeds many closed-source models in reasoning, context handling, and multilingual support, while remaining fully open-source and highly customizable.
Q2: Can I fine-tune Deep Seek v3.1 on my own data?
A: Yes! The open-source weights and documentation make it easy to fine-tune for domain-specific tasks.
Q3: What are the hardware requirements for running Deep Seek v3.1 locally?
A: The full model requires high-end GPUs (A100 or similar), but smaller versions are available for less resource-intensive deployments.
Q4: Is Deep Seek v3.1 suitable for enterprise applications?
A: Absolutely. With robust API support, agentic AI capabilities, and strong benchmarks, it’s ideal for enterprise-scale AI solutions.
Conclusion: The Future of Open-Source LLMs Starts Here
Deep Seek v3.1 is more than just another large language model—it’s a leap forward in open, accessible, and agentic AI. With its hybrid inference modes, massive context window, advanced reasoning, and multilingual prowess, it’s poised to power the next generation of AI applications across industries.
Whether you’re building autonomous agents, analyzing massive datasets, or creating multilingual content, Deep Seek v3.1 offers the flexibility, performance, and openness you need.
Artificial intelligence is evolving at an unprecedented pace, and large concept models (LCMs) represent the next big step in that journey. While large language models (LLMs) such as GPT-4 have revolutionized how machines generate and interpret text, LCMs go further: they are built to represent, connect, and reason about high-level concepts across multiple forms of data. In this blog, we’ll explore the technical underpinnings of LCMs, their architecture, components, and capabilities and examine how they are shaping the future of AI.
illustrated: visualization of reasoning in an embedding space of concepts (task of summarization) (source: https://arxiv.org/pdf/2412.08821)
Technical Overview of Large Concept Models
Large concept models (LCMs) are advanced AI systems designed to represent and reason over abstract concepts, relationships, and multi-modal data. Unlike LLMs, which primarily operate in the token or sentence space, LCMs focus on structured representations—often leveraging knowledge graphs, embeddings, and neural-symbolic integration.
Key Technical Features:
1. Concept Representation:
Large Concept Models encode entities, events, and abstract ideas as high-dimensional vectors (embeddings) that capture semantic and relational information.
2. Knowledge Graph Integration:
These models use knowledge graphs, where nodes represent concepts and edges denote relationships (e.g., “insulin resistance” —is-a→ “metabolic disorder”). This enables multi-hop reasoning and relational inference.
3. Multi-Modal Learning:
Large Concept Models process and integrate data from diverse modalities—text, images, structured tables, and even audio—using specialized encoders for each data type.
4. Reasoning Engine:
At their core, Large Concept Models employ neural architectures (such as graph neural networks) and symbolic reasoning modules to infer new relationships, answer complex queries, and provide interpretable outputs.
5. Interpretability:
Large Concept Models are designed to trace their reasoning paths, offering explanations for their outputs—crucial for domains like healthcare, finance, and scientific research.
fundamental architecture of an Large Concept Model (LCM). source: https://arxiv.org/pdf/2412.08821
A large concept model (LCM) is not a single monolithic network but a composite system that integrates multiple specialized components into a reasoning pipeline. Its architecture typically blends neural encoders, symbolic structures, and graph-based reasoning engines, working together to build and traverse a dynamic knowledge representation.
Core Components
1. Input Encoders
Text Encoder: Transformer-based architectures (e.g., BERT, T5, GPT-like) that map words and sentences into semantic embeddings.
Vision Encoder: CNNs, vision transformers (ViTs), or CLIP-style dual encoders that turn images into concept-level features.
Structured Data Encoder: Tabular encoders or relational transformers for databases, spreadsheets, and sensor logs.
Audio/Video Encoders: Sequence models (e.g., conformers) or multimodal transformers to process temporal signals.
These encoders normalize heterogeneous data into a shared embedding space where concepts can be compared and linked.
2. Concept Graph Builder
Constructs or updates a knowledge graph where nodes = concepts and edges = relations (hierarchies, causal links, temporal flows).
May rely on graph embedding techniques (e.g., TransE, RotatE, ComplEx) or schema-guided extraction from raw text.
Handles dynamic updates, so the graph evolves as new data streams in (important for enterprise or research domains).
Aligns embeddings across modalities into a unified concept space.
Often uses cross-attention mechanisms (like in CLIP or Flamingo) to ensure that, for example, an image of “insulin injection” links naturally with the textual concept of “diabetes treatment.”
May incorporate contrastive learning to force consistency across modalities.
4. Reasoning and Inference Module
The “brain” of the Large Concept Model, combining graph neural networks (GNNs), differentiable logic solvers, or neural-symbolic hybrids.
Capabilities:
Multi-hop reasoning (chaining concepts together across edges).
This layered architecture allows LCMs to scale across domains, adapt to new knowledge, and explain their reasoning—three qualities where LLMs often fall short.
Think of an Large Concept Model as a super-librarian. Instead of just finding books with the right keywords (like a search engine), this librarian understands the content, connects ideas across books, and can explain how different topics relate. If you ask a complex question, the librarian doesn’t just give you a list of books—they walk you through the reasoning, showing how information from different sources fits together.
Combining structured and unstructured data from multiple sources is complex and requires robust data engineering.
Model Complexity:
Building and maintaining large, dynamic concept graphs demands significant computational resources and expertise.
Bias and Fairness:
Ensuring that Large Concept Models provide fair and unbiased reasoning requires careful data curation and ongoing monitoring.
Evaluation:
Traditional benchmarks may not fully capture the reasoning and interpretability strengths of Large Concept Models.
Scalability:
Deploying LCMs at enterprise scale involves challenges in infrastructure, maintenance, and user adoption.
Conclusion & Further Reading
Large concept models represent a significant leap forward in artificial intelligence, enabling machines to reason over complex, multi-modal data and provide transparent, interpretable outputs. By combining technical rigor with accessible analogies, we can appreciate both the power and the promise of Large Concept Models for the future of AI.
Agentic AI marks a shift in how we think about artificial intelligence. Rather than being passive responders to prompts, agents are empowered thinkers and doers, capable of:
Analyzing and understanding complex tasks.
Planning and decomposing tasks into manageable steps.
Executing actions, invoking external tools, and adjusting strategies on the fly.
Yet, converting these sophisticated capabilities into scalable, reliable applications is nontrivial. That’s where the OpenAI Agents SDK shines. It serves as a trusted toolkit, giving developers modular primitives like tools, sessions, guardrails, and workflows—so you can focus on solving real problems, not reinventing orchestration logic.
Released in March 2025, the OpenAI Agents SDK is a lightweight, Python-first open-source framework built to orchestrate agentic workflows seamlessly. It’s designed around two guiding principles:
Minimalism with power: fewer abstractions, faster learning.
Opinionated defaults with room for flexibility: ready to use out of the box, but highly customizable.
Understanding the SDK’s architecture is crucial for effective agentic AI development. Here are the main components:
Agent
The Agent is the brain of your application. It defines instructions, memory, tools, and behavior. Think of it as a self-contained entity that listens, thinks, and acts. An agent doesn’t just generate text—it reasons through tasks and decides when to invoke tools.
Tool
Tools are how agents extend their capabilities. A tool can be a Python function (like searching a database) or an external API (like Notion, GitHub, or Slack). Tools are registered with metadata—name, input/output schema, and documentation—so that agents know when and how to use them.
Runner
The Runner manages execution. It’s like the conductor of an orchestra—receiving user input, handling retries, choosing tools, and streaming responses back.
ToolCall & ToolResponse
Instead of messy string passing, the SDK uses structured classes for agent-tool interactions. This ensures reliable communication and predictable error handling.
Guardrails
Guardrails enforce safety and reliability. For example, if an agent is tasked with booking a flight, a guardrail could ensure that the date format is valid before executing the action. This prevents runaway errors and unsafe outputs.
Tracing & Observability
One of the hardest parts of agentic systems is debugging. Tracing provides visual and textual insights into what the agent is doing—why it picked a certain tool, what inputs were passed, and where things failed.
Multi-Agent Workflows
Complex tasks often require collaboration. The SDK lets you compose multi-agent workflows, where one agent can hand off tasks to another. For instance, a “Research Agent” could gather data, then hand it off to a “Writer Agent” for report generation.
Here’s a minimal example using the OpenAI Agents SDK:
Output:
A creative haiku generated by the agent.
This “hello world” example highlights the simplicity of the SDK, you get agent loops, tool orchestration, and state handling without extra boilerplate.
Working with Tools Using the API
Tools extend agent capabilities by allowing them to interact with external systems. You can wrap any Python function as a tool using the function_tool decorator, or connect to MCP-compliant servers for remote tools.
The OpenAI Agents SDK includes robust tracing and observability tools:
Visual DAGs:
Visualize agent workflows and tool calls.
Execution Logs:
Track agent decisions, tool usage, and errors.
Integration:
Export traces to platforms like Logfire, AgentOps, or OpenTelemetry.
Debugging:
Pinpoint bottlenecks and optimize performance.
Enable Visualization:
Multi-Agent Workflows
The SDK supports orchestrating multiple agents for collaborative, modular workflows. Agents can delegate tasks (handoffs), chain outputs, or operate in parallel.
The OpenAI Agents SDK is a powerful, production-ready toolkit for agentic AI development. By leveraging its modular architecture, tool integrations, guardrails, tracing, and multi-agent orchestration, developers can build reliable, scalable agents for real-world tasks.
Ready to build agentic AI?
Explore more at Data Science Dojo’s blog and start your journey with the OpenAI Agents SDK.
The landscape of artificial intelligence is rapidly evolving, and OpenAI’sDeep Research feature for ChatGPT marks a pivotal leap toward truly autonomous AI research agents. Unlike traditional chatbots or simple web-browsing tools, Deep Research empowers ChatGPT to independently plan, execute, and synthesize complex research tasks, delivering structured, cited reports that rival human analysts. As competitors like Google Gemini, DeepSeek, xAI Grok, and Perplexity AI race to develop similar capabilities, understanding the technical underpinnings, practical applications, and broader implications of Deep Research is essential for anyone invested in the future of AI.
In this comprehensive guide, we’ll dive deep into OpenAI’s Deep Research: its technical architecture, workflow, release timeline, usage limits, competitive comparisons, real-world use cases, limitations, risks, and its significance for the next generation of autonomous AI research.
Timeline of Release: How Deep Research Evolved
OpenAI’s Deep Research feature was officially launched for ChatGPT on February 3, 2025, initially targeting Pro subscribers. The rollout was strategic, reflecting both the technical complexity and the need for responsible deployment:
February 2025:
Deep Research debuts for ChatGPT Pro ($200/month), leveraging the o3 model for advanced, multi-step research.
April 2025:
A “lightweight” Deep Research version (o4-mini) is introduced for Plus, Team, and Enterprise users, offering faster but less thorough research capabilities.
June 2025:
Expanded quotas and limited access for free users, democratizing the feature while maintaining safeguards.
OpenAI Deep Research scored 26.6%on Humanity’s Last Exam (a benchmark for expert-level reasoning across 100 subjects), outperforming DeepSeek R1 (9.4%) and GPT-4o (3.3%).
Google Gemini and Perplexity AI offer strong citation and web coverage but are generally less thorough in multi-step reasoning.
OpenAI’s Deep Research is more than just a feature, it’s a glimpse into the future of autonomous AI agents capable of handling complex, time-consuming research tasks with minimal human intervention. This shift from reactive Q&A to proactive, agentic investigation has profound implications:
Knowledge Work Transformation:
Automates research tasks in law, finance, healthcare, academia, and journalism.
Frees up human experts for higher-level analysis and decision-making.
Democratization of Expertise:
Makes advanced research accessible to non-experts, leveling the playing field.
Continuous Learning:
AI agents can update their knowledge bases in real time, staying current with the latest developments.
Ethical Imperatives:
As AI agents gain autonomy, robust safeguards, transparency, and human oversight become even more critical.
Conclusion
OpenAI’s Deep Research for ChatGPT represents a watershed moment in the evolution of AI—from conversational assistants to autonomous research agents. By combining advanced planning, multi-modal retrieval, and structured synthesis, Deep Research delivers insights that are deeper, more transparent, and more actionable than ever before. As competitors race to match these capabilities, and as real-world applications multiply, the significance of autonomous AI research will only grow.
However, with great power comes great responsibility. Ensuring accuracy, mitigating bias, and maintaining transparency are essential as we entrust AI with ever more complex research tasks. The future of knowledge work is here—and it’s agentic, autonomous, and deeply transformative.
FAQ
Q: What is OpenAI’s Deep Research feature?
A: It’s an autonomous research mode in ChatGPT that plans, executes, and synthesizes multi-step research tasks, delivering structured, cited reports.
Q: Who can access Deep Research?
A: Pro subscribers get full access; Plus, Team, and Enterprise users get a lightweight version; free users have limited queries.
Q: How does Deep Research differ from standard ChatGPT browsing?
A: Deep Research is proactive, multi-step, and can run for up to 30 minutes, whereas standard browsing is reactive and single-pass.
Q: What are the main competitors?
A: Google Gemini, DeepSeek R1, xAI Grok, and Perplexity AI all offer similar research agents, but with varying depth and transparency.
Q: What are the risks?
A: Hallucinations, bias, quota limits, and the need for human verification remain key challenges.
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.
source: blog.ai-futures.org
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 1.5billion 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.
Codex was a specialized branch of OpenAI models fine-tuned from GPT-3 to excel at programming tasks. It powered GitHubCopilot 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.
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.
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.
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.
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.
Each generation has exponentially increased in size and capability.
Multimodal Integration
Moving from text-only to multi-input processing.
Alignment and Safety
Increasing focus on AI ethics and responsible deployment.
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.
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 launchedGPT‑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.
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.
source: latent.space
Sample Prompt for Beginners:
“Explain how photosynthesis works, step by step.”
“Think carefully and help me plan a weekly budget.”
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?”
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.
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):
In ChatGPT or Copilot, look for the “upload” button.
Select your image or audio file.
Type a prompt like “Describe this image” or “Transcribe this audio.”
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.
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
Indexing: Documents are chunked and embedded into a vector database.
Retrieval: At query time, the system finds the most semantically relevant chunks using vector similarity search.
Augmentation: Retrieved context is concatenated with the user’s prompt and fed to the LLM.
Generation: The LLM produces a grounded, context-aware response.
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.
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.
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
source: infogain
Start with RAG:
Use traditional RAG for unstructured data and simple Q&A.
Adopt Graph RAG for Complexity:
When queries require multi-hop reasoning or relationship mapping, transition to graph rag.
Leverage Hybrid Approaches:
Combine vector search and graph traversal for maximum coverage.
Monitor and Benchmark:
Use hybrid scorecards to track both AI quality and engineering velocity.
Iterate Relentlessly:
Experiment with chunking, retrieval, and prompt formats for optimal results.
Treat Context as a Product:
Apply version control, quality checks, and continuous improvement to your context pipelines.
Structure Prompts Clearly:
Separate instructions, context, and queries for clarity.
Leverage In-Context Learning:
Provide high-quality examples in the prompt.
Security and Compliance:
Guard against prompt injection, data leakage, and unauthorized tool use.
Ethics and Privacy:
Ensure responsible use of interconnected personal or proprietary data.
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.
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.
