Implementing Retrieval Augmented Generation in Enterprises

𝗥𝗔𝗚 𝗗𝗲𝘃𝗲𝗹𝗼𝗽𝗲𝗿’𝘀 𝗦𝘁𝗮𝗰𝗸 — 𝗪𝗵𝗮𝘁 𝗬𝗼𝘂 𝗡𝗲𝗲𝗱 𝘁𝗼 𝗞𝗻𝗼𝘄 𝗕𝗲𝗳𝗼𝗿𝗲 𝗕𝘂𝗶𝗹𝗱𝗶𝗻𝗴 Building with Retrieval-Augmented Generation (RAG) isn't just about choosing the right LLM. It's about assembling an entire stack—one that's modular, scalable, and future-proof. This visual from Kalyan KS neatly categorizes the current RAG landscape into actionable layers: → 𝗟𝗟𝗠𝘀 (𝗢𝗽𝗲𝗻 𝘃𝘀 𝗖𝗹𝗼𝘀𝗲𝗱) Open models like LLaMA 3, Phi-4, and Mistral offer control and customization. Closed models (OpenAI, Claude, Gemini) bring powerful performance with less overhead. Your tradeoff: flexibility vs convenience. → 𝗙𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸𝘀 LangChain, LlamaIndex, Haystack, and txtai are now essential for building orchestrated, multi-step AI workflows. These tools handle chaining, memory, routing, and tool-use logic behind the scenes. → 𝗩𝗲𝗰𝘁𝗼𝗿 𝗗𝗮𝘁𝗮𝗯𝗮𝘀𝗲𝘀 Chroma, Qdrant, Weaviate, Milvus, and others power the retrieval engine behind every RAG system. Low-latency search, hybrid scoring, and scalable indexing are key to relevance. → 𝗗𝗮𝘁𝗮 𝗘𝘅𝘁𝗿𝗮𝗰𝘁𝗶𝗼𝗻 (𝗪𝗲𝗯 + 𝗗𝗼𝗰𝘀) Whether you're crawling the web (Crawl4AI, FireCrawl) or parsing PDFs (LlamaParse, Docling), raw data access is non-negotiable. No context means no quality answers. → 𝗢𝗽𝗲𝗻 𝗟𝗟𝗠 𝗔𝗰𝗰𝗲𝘀𝘀 Platforms like Hugging Face, Ollama, Groq, and Together AI abstract away infra complexity and speed up experimentation across models. → 𝗧𝗲𝘅𝘁 𝗘𝗺𝗯𝗲𝗱𝗱𝗶𝗻𝗴𝘀 The quality of retrieval starts here. Open-source models (Nomic, SBERT, BGE) are gaining ground, but proprietary offerings (OpenAI, Google, Cohere) still dominate enterprise use. → 𝗘𝘃𝗮𝗹𝘂𝗮𝘁𝗶𝗼𝗻 Tools like Ragas, Trulens, and Giskard bring much-needed observability—measuring hallucinations, relevance, grounding, and model behavior under pressure. 𝗧𝗮𝗸𝗲𝗮𝘄𝗮𝘆: RAG is not just an integration problem. It’s a design problem. Each layer of this stack requires deliberate choices that impact latency, quality, explainability, and cost. If you're serious about GenAI, it's time to think in terms of stacks—not just models. What does your RAG stack look like today?

Context-aware agents require deliberate architecture that combines retrieval-augmented generation, session memory, and adaptive reasoning. This 10-step framework begins with defining the agent’s domain, use cases, and output structure, followed by ingestion and chunking of trustworthy data aligned to safety and alignment principles. Embeddings are then generated using models like OpenAI or Cohere and stored in vector databases such as FAISS or Pinecone for efficient semantic retrieval. Retrieval logic leverages k-NN search to fetch relevant chunks based on similarity and metadata filters. Prompts are engineered dynamically using retrieved context, optionally enriched with few-shot examples, and sent to LLMs like GPT-4 or Claude with configurable parameters. Session memory can be integrated to track interaction history and enhance continuity. Continuous evaluation identifies hallucinations, prompt failures, and edge cases for iterative refinement. Deployment involves wrapping the agent in an API or interface with monitoring hooks, and expansion includes tool use, personalization, and self-corrective mechanisms. If you follow this framework, you’ll be building the pipeline forming the backbone of production-grade AI agents that reason with context and respond with precision. Go build! #genai #aiagent #artificialintelligence

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

Why 90% of Candidates Fail RAG (Retrieval-Augmented Generation) Interviews You know how to call the OpenAI API. You’ve built a chatbot using LangChain. You’ve even added a vector database like Pinecone or FAISS. But then the interview happens: • Design a multilingual enterprise RAG pipeline • Optimize retrieval latency for 100M documents • Implement query understanding with hybrid search • Build guardrails for hallucination control in production Sound familiar? Most candidates freeze because they’ve only built “toy RAG demos”—never thought about enterprise-scale RAG systems. ⸻ The gap isn’t retrieval—it’s end-to-end RAG system design. Here’s what top candidates do differently: • Instead of: I’ll just embed documents and query them They ask: How do I chunk documents optimally, avoid semantic drift, and handle multilingual embeddings? • Instead of: I’ll just store vectors in Pinecone They ask: How do I design tiered storage (hot vs. cold), caching, and hybrid retrieval (BM25 + dense) to balance speed and accuracy? • Instead of: I’ll let the LLM generate answers They ask: How do I add rerankers, context window optimizers, and confidence scoring to minimize hallucinations? • Instead of: I’ll just call GPT-4 They ask: How do I implement cost-aware routing (open-source models first, GPT fallback) with prompt optimization? ⸻ Why senior AI engineers stand out They don’t just connect an LLM to a database—they design scalable, resilient, and explainable RAG ecosystems. They think about: • Retrieval accuracy vs. latency trade-offs • Vector DB sharding and replication strategies • Monitoring retrieval quality & query drift • Governance: logging, traceability, and compliance That’s why they clear FAANG and top AI company interviews. ⸻ My practice scenarios To prepare, I’ve been tackling real RAG system design challenges like: 1. Designing a multilingual enterprise RAG pipeline with cross-lingual embeddings. 2. Building a retrieval layer with hybrid search + rerankers for better precision. 3. Designing a caching and cost-optimization strategy for high-traffic RAG systems. 4. Implementing guardrails with policy-based filtering and hallucination detection. 5. Architecting RAG pipelines with orchestration tools like LangGraph or n8n. 👉 Most fail because they focus on the model, not the retrieval architecture + system design. Those who succeed show they can build ChatGPT-like RAG systems at scale. If you found this helpful, please like & share—it’ll help others prepping for RAG interviews too.

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.

Excited to share our production guide for building RAG-based LLM applications where we bridge the gap between OSS and closed-source LLMs. - 💻 Develop a retrieval augmented generation (RAG) LLM app from scratch. - 🚀 Scale the major workloads (load, chunk, embed, index, serve, etc.) across multiple workers. - ✅ Evaluate different configurations of our application to optimize for both per-component (ex. retrieval_score) and overall performance (quality_score). - 🔀 Implement LLM hybrid routing approach to bridge the gap b/w OSS and closed-source LLMs. - 📦 Serve the application in a highly scalable and available manner. - 💥 Share the 1st order and 2nd order impacts LLM applications have had on our products and org. 🔗 Links: - Blog post (45 min. read): https://lnkd.in/g34a9Zwp - GitHub repo: https://lnkd.in/g3zHFD5z - Interactive notebook: https://lnkd.in/g8ghFWm9 Philipp Moritz and I had a blast developing and productionizing this with the Anyscale team and we're excited to share Part II soon (more details in the blog post).