Retrieval-Augmented Generation Technology Stack Guide

If you’re an AI engineer trying to understand and build with GenAI, RAG (Retrieval-Augmented Generation) is one of the most essential components to master. It’s the backbone of any LLM system that needs fresh, accurate, and context-aware outputs. Let’s break down how RAG works, step by step, from an engineering lens, not a hype one: 🧠 How RAG Works (Under the Hood) 1. Embed your knowledge base → Start with unstructured sources - docs, PDFs, internal wikis, etc. → Convert them into semantic vector representations using embedding models (e.g., OpenAI, Cohere, or HuggingFace models) → Output: N-dimensional vectors that preserve meaning across contexts 2. Store in a vector database → Use a vector store like Pinecone, Weaviate, or FAISS → Index embeddings to enable fast similarity search (cosine, dot-product, etc.) 3. Query comes in - embed that too → The user prompt is embedded using the same embedding model → Perform a top-k nearest neighbor search to fetch the most relevant document chunks 4. Context injection → Combine retrieved chunks with the user query → Format this into a structured prompt for the generation model (e.g., Mistral, Claude, Llama) 5. Generate the final output → LLM uses both the query and retrieved context to generate a grounded, context-rich response → Minimizes hallucinations and improves factuality at inference time 📚 What changes with RAG? Without RAG: 🧠 “I don’t have data on that.” With RAG: 🤖 “Based on [retrieved source], here’s what’s currently known…” Same model, drastically improved quality. 🔍 Why this matters You need RAG when: → Your data changes daily (support tickets, news, policies) → You can’t afford hallucinations (legal, finance, compliance) → You want your LLMs to access your private knowledge base without retraining It’s the most flexible, production-grade approach to bridge static models with dynamic information. 🛠️ Arvind and I are kicking off a hands-on workshop on RAG This first session is designed for beginner to intermediate practitioners who want to move beyond theory and actually build. Here’s what you’ll learn: → How RAG enhances LLMs with real-time, contextual data → Core concepts: vector DBs, indexing, reranking, fusion → Build a working RAG pipeline using LangChain + Pinecone → Explore no-code/low-code setups and real-world use cases If you're serious about building with LLMs, this is where you start. 📅 Save your seat and join us live: https://lnkd.in/gS_B7_7d

Agentic AI Is Promising, But RAG Has Been Doing the Heavy Lifting for Years While Agentic AI continues to evolve, it's Retrieval-Augmented Generation (RAG) that has powered some of the most practical, production-ready AI applications over the past 2–3 years. From enterprise search to chatbots, copilots, and domain-specific QA systems—RAG is the backbone of many GenAI solutions in use today. To help navigate this growing ecosystem, here’s a breakdown of the modern RAG Developer Stack, covering all critical components: ⫸ LLMs – Open-source (e.g., LLaMA 3, Mistral, Qwen) and proprietary (e.g., OpenAI, Claude, Gemini) ⫸ Frameworks – LangChain, LlamaIndex, Haystack, Txtai ⫸ Vector Databases – Chroma, Pinecone, Qdrant, Weaviate, Milvus ⫸ Data Extraction – Tools for web and document ingestion like Crawl4AI, MegaParser, Docling ⫸ Text Embeddings – Open (SBERT, Ollama) and closed (OpenAI, Cohere, Gemini) ⫸ Open LLM Access – Groq, Together AI, Hugging Face, Ollama ⫸ Evaluation Tools – Giskard, Ragas, Trulens for observability, feedback loops, and trust Each layer plays a critical role—from reducing hallucinations to improving latency and enabling real-time responses. ➤ Which part of the RAG stack do you find most challenging or exciting to work with?

🗄️ Retrieval Augmented Generation (RAG) • http://rag.aman.ai - RAG combines information retrieval with LLMs for enhanced response generation using an external knowledge base.  - This RAG primer delves into various facets of RAG encompassing chunking, embedding creation, indexing strategies, and evaluation. ➡️ For more AI primers, follow me on X at: http://x.aman.ai 🔹 Neural Retrieval 🔹 RAG Pipeline 🔹 Benefits of RAG  🔹 RAG vs. Fine-tuning  🔹 Ensemble of RAG 🔹 Choosing a Vector DB using a Feature Matrix 🔹 Building a RAG Pipeline - Ingestion - Chunking - Embeddings - Sentence Embeddings - Retrieval (Standard/Naive Approach, Sentence Window Retrieval Pipeline, Auto-merging Retriever) - Retrieve Approximate Nearest Neighbors - Response Generation / Synthesis (Lost in the Middle, The “Need in a Haystack” Test) 🔹 Component-Wise Evaluation - Retrieval Metrics (Context Precision, Context Recall, Context Relevance) - Generation Metrics (Groundedness/Faithfulness, Answer Relevance) - End-to-End Evaluation: Answer Semantic Similarity, Answer Correctness 🔹 Multimodal RAG 🔹 Improving RAG Systems - Re-ranking Retrieved Results - FLARE Technique - HyDE 🔹 Related Papers - Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks - MuRAG: Multimodal Retrieval-Augmented Generator - Active Retrieval Augmented Generation (FLARE) - Fine-Tuning or Retrieval? Comparing Knowledge Injection in LLMs - Dense X Retrieval: What Retrieval Granularity Should We Use? - ARES: an Automated Evaluation Framework for Retrieval-Augmented Generation Systems - Hypothetical Document Embeddings (HyDE) ✍🏼 Primer written in collaboration with Vinija Jain #artificialintelligence #machinelearning #deeplearning #neuralnetworks