Rag

Introduction The difference between a chatbot and an agent isn’t just autonomy—it’s memory. A chatbot responds to each message in isolation. An agent remembers context, learns from outcomes, and evolves behavior over time. Agent memory is the system that enables this persistence: storing relevant information, retrieving it when needed, updating beliefs as reality changes, and forgetting what’s no longer relevant. Without memory, agents can’t maintain long-term goals, learn from mistakes, or provide consistent experiences. ...

Introduction Traditional RAG systems treat knowledge as a collection of text chunks—embedded, indexed, and retrieved based on semantic similarity. This works well for simple factual lookup, but fails when questions require understanding relationships, dependencies, or multi-hop reasoning. Graph RAG fundamentally reimagines how knowledge is represented: instead of flat documents, information is structured as a graph of entities and relationships. This enables LLMs to traverse connections, follow dependencies, and reason about how concepts relate to each other. ...

Introduction Retrieval-Augmented Generation (RAG) transformed how LLMs access external knowledge. But traditional RAG has a fundamental limitation: it’s passive. You retrieve once, hope it’s relevant, and generate an answer. If the retrieval fails, the entire system fails. Agentic RAG changes this paradigm. Instead of a single retrieve-then-generate pass, an AI agent actively plans retrieval strategies, evaluates results, reformulates queries, and iterates until it finds sufficient information—or determines that it cannot. ...

Introduction Retrieval-Augmented Generation (RAG) has become a foundational pattern for building accurate, scalable, and fact-grounded applications with large language models (LLMs). At its core, RAG combines a retrieval component (to fetch relevant pieces of knowledge) with a generation component (the LLM) that produces answers conditioned on that retrieved context. This article breaks RAG down from first principles: the indexing and retrieval stages, the augmentation of prompts, the generation step, common challenges, practical mitigations, and code examples to get you started. ...

Table of contents Introduction What is RAG (Retrieval-Augmented Generation)? Why RAG matters: strengths and limitations Core RAG components and pipeline Retriever types Vector stores and embeddings Indexing and metadata Reader / generator models Orchestration and caching Chunking strategies (text segmentation) Fixed-size chunking Overlap and stride Semantic chunking Structure-aware and LLM-based chunking Practical guidelines Embeddings: models, training, and best practices Off-the-shelf vs. fine-tuned embeddings Dimensionality, normalization, and distance metrics Handling multilingual and multimodal data Vector search and hybrid retrieval ANN algorithms and trade-offs Hybrid (BM25 + vector) search patterns Scoring, normalization, and retrieval thresholds Reranking and cross-encoders First-stage vs. second-stage retrieval Cross-encoder rerankers: when and how to use them Efficiency tips (distillation, negative sampling) Query rewriting and query engineering User intent detection and canonicalization Query expansion, paraphrasing, and reciprocal-rank fusion Multi-query strategies for coverage Context management and hallucination reduction Context window budgeting and token economics Autocut / context trimming strategies Source attribution and provenance Multi-hop, iterative retrieval, and reasoning Decomposition and stepwise retrieval GraphRAG and retrieval over knowledge graphs Chaining retrievers with reasoning agents Context distillation and chunk selection strategies Condensing retrieved documents Evidence aggregation patterns Using LLMs to produce distilled context Fine-tuning and retrieval-aware training Fine-tuning LLMs for RAG (instruction, RLHF considerations) Training retrieval models end-to-end (RAG-style training) Retrieval-augmented pretraining approaches Memory and long-term context Short-term vs. long-term memories Vector memories and episodic memory patterns Freshness, TTL, and incremental updates Evaluation: metrics and test frameworks Precision / Recall / MRR / nDCG for retrieval Factuality, hallucination rate, and human evaluation for generation Establishing gold-standard evidence sets and benchmarks Operational concerns: scaling, monitoring, and safety Latency and throughput optimization Cost control (compute, storage, embedding calls) Access control, data privacy, and redaction Explainability and user-facing citations Advanced topics and research directions Multimodal RAG (images, audio, tables) Graph-based retrieval and retrieval-aware LLM architectures Retrieval for agents and tool-use workflows Recipes: end-to-end examples and code sketches Minimal RAG pipeline (conceptual) Practical LangChain / LlamaIndex style pattern (pseudo-code) Reranker integration example (pseudo-code) Troubleshooting: common failure modes and fixes Checklist: production-readiness before launch Conclusion Resources and further reading Introduction This post is a practical, end-to-end guide to Retrieval-Augmented Generation (RAG). It’s aimed at engineers, ML practitioners, product managers, and technical writers who want to go from RAG basics to advanced production patterns. The goal is to provide both conceptual clarity and hands-on tactics so you can design, build, evaluate, and operate robust RAG systems. ...