How to Use Retrieval Augmented Generation Strategies

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

In the world of Generative AI, 𝗥𝗲𝘁𝗿𝗶𝗲𝘃𝗮𝗹-𝗔𝘂𝗴𝗺𝗲𝗻𝘁𝗲𝗱 𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝗼𝗻 (𝗥𝗔𝗚) is a game-changer. By combining the capabilities of LLMs with domain-specific knowledge retrieval, RAG enables smarter, more relevant AI-driven solutions. But to truly leverage its potential, we must follow some essential 𝗯𝗲𝘀𝘁 𝗽𝗿𝗮𝗰𝘁𝗶𝗰𝗲𝘀: 1️⃣ 𝗦𝘁𝗮𝗿𝘁 𝘄𝗶𝘁𝗵 𝗮 𝗖𝗹𝗲𝗮𝗿 𝗨𝘀𝗲 𝗖𝗮𝘀𝗲 Define your problem statement. Whether it’s building intelligent chatbots, document summarization, or customer support systems, clarity on the goal ensures efficient implementation. 2️⃣ 𝗖𝗵𝗼𝗼𝘀𝗲 𝘁𝗵𝗲 𝗥𝗶𝗴𝗵𝘁 𝗞𝗻𝗼𝘄𝗹𝗲𝗱𝗴𝗲 𝗕𝗮𝘀𝗲 - Ensure your knowledge base is 𝗵𝗶𝗴𝗵-𝗾𝘂𝗮𝗹𝗶𝘁𝘆, 𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲𝗱, 𝗮𝗻𝗱 𝘂𝗽-𝘁𝗼-𝗱𝗮𝘁𝗲. - Use vector embeddings (e.g., pgvector in PostgreSQL) to represent your data for efficient similarity search. 3️⃣ 𝗢𝗽𝘁𝗶𝗺𝗶𝘇𝗲 𝗥𝗲𝘁𝗿𝗶𝗲𝘃𝗮𝗹 𝗠𝗲𝗰𝗵𝗮𝗻𝗶𝘀𝗺𝘀 - Use hybrid search techniques (semantic + keyword search) for better precision. - Tools like 𝗽𝗴𝗔𝗜, 𝗪𝗲𝗮𝘃𝗶𝗮𝘁𝗲, or 𝗣𝗶𝗻𝗲𝗰𝗼𝗻𝗲 can enhance retrieval speed and accuracy. 4️⃣ 𝗙𝗶𝗻𝗲-𝗧𝘂𝗻𝗲 𝗬𝗼𝘂𝗿 𝗟𝗟𝗠 (𝗢𝗽𝘁𝗶𝗼𝗻𝗮𝗹) - If your use case demands it, fine-tune the LLM on your domain-specific data for improved contextual understanding. 5️⃣ 𝗘𝗻𝘀𝘂𝗿𝗲 𝗦𝗰𝗮𝗹𝗮𝗯𝗶𝗹𝗶𝘁𝘆 - Architect your solution to scale. Use caching, indexing, and distributed architectures to handle growing data and user demands. 6️⃣ 𝗠𝗼𝗻𝗶𝘁𝗼𝗿 𝗮𝗻𝗱 𝗜𝘁𝗲𝗿𝗮𝘁𝗲 - Continuously monitor performance using metrics like retrieval accuracy, response time, and user satisfaction. - Incorporate feedback loops to refine your knowledge base and model performance. 7️⃣ 𝗦𝘁𝗮𝘆 𝗦𝗲𝗰𝘂𝗿𝗲 𝗮𝗻𝗱 𝗖𝗼𝗺𝗽𝗹𝗶𝗮𝗻𝘁 - Handle sensitive data responsibly with encryption and access controls. - Ensure compliance with industry standards (e.g., GDPR, HIPAA). With the right practices, you can unlock its full potential to build powerful, domain-specific AI applications. What are your top tips or challenges?

Title: RAG (Retrieval-Augmented Generation) Best Practices Retrieval-Augmented Generation (RAG) is a powerful technique that combines the capabilities of Large Language Models (LLMs) with external knowledge retrieval to deliver highly relevant and accurate responses. Here’s a comprehensive guide to RAG best practices, as outlined in the attached diagram: Key Components of RAG: 1️⃣ Evaluation: Test the general performance, domain-specific accuracy, and retrieval capability of your system to ensure it aligns with your application’s goals. 2️⃣ Fine-Tuning: Experiment with different strategies such as Disturb, Random, or Normal initialization to optimize LLM performance for your use case. 3️⃣ Summarization: Choose between Extractive (e.g., BM25, Contriever) or Abstractive (e.g., LongLLMlingua, SelectiveContext) approaches based on your summarization needs. 4️⃣ Query Classification: Enable the LLM to classify queries effectively, ensuring that the right retrieval strategy is used for each query type. 5️⃣ Retrieval Techniques: Utilize diverse retrieval strategies such as: BM25 for traditional retrieval. Hybrid Search (HyDE or HyDE+Hybrid) for combining embedding-based and keyword-based search. Query Rewriting and Query Decomposition for complex queries. 6️⃣ Embedding: Use advanced embedding models like intfloat/e5, Jina-embeddings-v2, or all-mpnet-base-v2 to generate high-quality vector representations. 7️⃣ Vector Database: Leverage robust vector databases like Milvus, Faiss, Weaviate, or Chroma for storing and retrieving embeddings efficiently. 8️⃣ Repacking and Reranking: Refine retrieval results through repacking (forward or reverse) and reranking using advanced techniques like monoT5 or RankLlmAM. Why RAG Matters: RAG allows you to go beyond static LLM responses by dynamically integrating external knowledge. This makes it ideal for use cases like question answering, document summarization, and domain-specific applications. Pro Tip: Effective chunking, embedding selection, and retrieval optimization are critical to building a scalable and high-performing RAG pipeline. Are you exploring RAG for your AI solutions? What challenges have you faced, and how have you addressed them? Let’s discuss insights and best practices for leveraging RAG to its fullest potential.

RAG just got smarter. If you’ve been working with Retrieval-Augmented Generation (RAG), you probably know the basic setup: An LLM retrieves documents based on a query and uses them to generate better, grounded responses. But as use cases get more complex, we need more advanced retrieval strategies—and that’s where these four techniques come in: Self-Query Retriever Instead of relying on static prompts, the model creates its own structured query based on metadata. Let’s say a user asks: “What are the reviews with a score greater than 7 that say bad things about the movie?” This technique breaks that down into query + filter logic, letting the model interact directly with structured data (like Chroma DB) using the right filters. Parent Document Retriever Here, retrieval happens in two stages: 1. Identify the most relevant chunks 2. Pull in their parent documents for full context This ensures you don’t lose meaning just because information was split across small segments. Contextual Compression Retriever (Reranker) Sometimes the top retrieved documents are… close, but not quite right. This reranker pulls the top K (say 4) documents, then uses a transformer + reranker (like Cohere) to compress and re-rank the results based on both query and context—keeping only the most relevant bits. Multi-Vector Retrieval Architecture Instead of matching a single vector per document, this method breaks both queries and documents into multiple token-level vectors using models like ColBERT. The retrieval happens across all vectors—giving you higher recall and more precise results for dense, knowledge-rich tasks. These aren’t just fancy tricks. They solve real-world problems like: • “My agent’s answer missed part of the doc.” • “Why is the model returning irrelevant data?” • “How can I ground this LLM more effectively in enterprise knowledge?” As RAG continues to scale, these kinds of techniques are becoming foundational. So if you’re building search-heavy or knowledge-aware AI systems, it’s time to level up beyond basic retrieval. Which of these approaches are you most excited to experiment with? #ai #agents #rag #theravitshow

TL;DR: RAG (Retrieval Augmented Generation) is the most common GenAI pattern but getting it work for enterprise use cases is not easy at all. With the latest release, Amazon Web Services (AWS) Bedrock’s knowledge bases (for RAG) maybe the best managed RAG offering that overcomes the most common RAG blockers. Naive RAG has 4 phases: 𝟎. 𝐈𝐧𝐝𝐞𝐱𝐢𝐧𝐠 – Create an (vector) index of data 𝟏. 𝐐𝐮𝐞𝐫𝐲 – User issues Query 𝟐. 𝐑𝐞𝐭𝐫𝐢𝐞𝐯𝐚𝐥 – Data is retrieved based on query 𝟑. 𝐆𝐞𝐧𝐞𝐫𝐚𝐭𝐢𝐨𝐧 – Data is fed to LLM to generate a response. But naive RAG has 7 failure points: (https://lnkd.in/ehAqbYbj) 𝟏. 𝐌𝐢𝐬𝐬𝐢𝐧𝐠 𝐂𝐨𝐧𝐭𝐞𝐧𝐭 – When user query is not in index, it can hallucinate a response 𝟐. 𝐌𝐢𝐬𝐬𝐞𝐝 𝐭𝐡𝐞 𝐓𝐨𝐩 𝐑𝐚𝐧𝐤𝐞𝐝 𝐃𝐨𝐜𝐮𝐦𝐞𝐧𝐭𝐬 - The answer to the question is in the document but did not rank highly enough to be returned. 𝟑. 𝐍𝐨𝐭 𝐢𝐧 𝐂𝐨𝐧𝐭𝐞𝐱𝐭 - Docs with the answer were retrieved from the database but did not make it into the context for generating an answer. 𝟒. 𝐍𝐨𝐭 𝐄𝐱𝐭𝐫𝐚𝐜𝐭𝐞𝐝 - The answer is present in the context, but the LLM failed to extract out the correct answer. 𝟓. 𝐖𝐫𝐨𝐧𝐠 𝐅𝐨𝐫𝐦𝐚𝐭 - The question involved extracting information in a certain format such as a table or list & the LLM ignored the instruction. 𝟔. 𝐈𝐧𝐜𝐨𝐫𝐫𝐞𝐜𝐭 𝐒𝐩𝐞𝐜𝐢𝐟𝐢𝐜𝐢𝐭𝐲 - The answer is returned in the response but is not specific enough or is too specific to address the query. 𝟕. 𝐈𝐧𝐜𝐨𝐦𝐩𝐥𝐞𝐭𝐞 response Amazon Bedrock’s Knowledge Base (KB) has grown to address all the above & then some. Here is the latest that Bedrock offers at each RAG stage: • 𝐃𝐚𝐭𝐚 𝐒𝐨𝐮𝐫𝐜𝐞𝐬 – S3, Web, Salesforce, SharePoint, Confluence     • 𝐈𝐧𝐝𝐞𝐱𝐢𝐧𝐠 – Source documents/data is chunked for retrieval & chunking strategies can significantly impact quality. Bedrock supports multiple chunking techniques – 𝐅𝐢𝐱𝐞𝐝, 𝐇𝐢𝐞𝐫𝐚𝐫𝐜𝐡𝐢𝐜𝐚𝐥, 𝐒𝐞𝐦𝐚𝐧𝐭𝐢𝐜 & even 𝐂𝐮𝐬𝐭𝐨𝐦 𝐂𝐡𝐮𝐧𝐤𝐢𝐧𝐠 via Lambda(!!) • 𝐐𝐮𝐞𝐫𝐲 𝐑𝐞𝐟𝐨𝐫𝐦𝐮𝐥𝐚𝐭𝐢𝐨𝐧 – Bedrock takes a complex input query & breaks it into multiple sub-queries. These sub-queries will then separately go through their own retrieval steps to find relevant chunks. • 𝐑𝐞𝐭𝐫𝐢𝐞𝐯𝐚𝐥 •  𝐇𝐲𝐛𝐫𝐢𝐝 𝐒𝐞𝐚𝐫𝐜𝐡 – Combine Keyword, Semantic or Hybrid search of data sources to improve retrieval quality •  𝐕𝐞𝐜𝐭𝐨𝐫 𝐃𝐁 𝐒𝐮𝐩𝐩𝐨𝐫𝐭 – OpenSearch, Pinecone, Mongo, Redis & Aurora •  𝐀𝐝𝐯𝐚𝐧𝐜𝐞𝐝 𝐏𝐚𝐫𝐬𝐢𝐧𝐠 – Bedrock provides the option to use FMs for parsing complex documents such as .pdf files with nested tables or text within images. •  𝐌𝐞𝐭𝐚𝐝𝐚𝐭𝐚 𝐟𝐢𝐥𝐭𝐞𝐫𝐢𝐧𝐠 to limit search aperture •  𝐂𝐢𝐭𝐚𝐭𝐢𝐨𝐧 𝐓𝐫𝐚𝐜𝐤𝐢𝐧𝐠 when providing responses •  𝐂𝐨𝐧𝐭𝐞𝐱𝐭𝐮𝐚𝐥 𝐆𝐫𝐨𝐮𝐧𝐝𝐢𝐧𝐠 – Combined with Guardrails Bedrock can reduce hallucinations even further KBs are not perfect but if you want to RAG on AWS then KBs today are your best bet! (Listen to Matt Wood talk about KBs: https://bit.ly/3S57FOq)