Building an LLM application that doesn't hallucinate? It’s all about the context. 🧠 I put together this high-level architecture breakdown of a Retrieval-Augmented Generation (RAG) pipeline to visualize how we can ground large language models in custom, domain-specific knowledge bases. While experimenting with these systems, here are the core stages I focus on to ensure accuracy: 1️⃣ Document Ingestion: Efficiently chunking and passing custom data (PDFs, APIs) through an embedding model into a vector database (like Pinecone or Chroma). 2️⃣ Semantic Search: Retrieving the 'Top-K' most relevant context chunks when a user submits a query. 3️⃣ Prompt Augmentation: Dynamically orchestrating the original query alongside the retrieved context. 4️⃣ Generation: Passing the enriched prompt to the LLM (like Qwen, Llama, or GPT) to generate a grounded response. 5️⃣ Continuous Evaluation: The most critical step. Using frameworks like RAGAS or TruLens to rigorously measure faithfulness, answer relevance, and context recall. Building the pipeline is only half the battle; evaluating its true effectiveness is what makes it production-ready. What frameworks are you currently preferring for your vector databases and RAG evaluation? Let’s discuss below! 👇 #GenerativeAI #MachineLearning #RAG #ArtificialIntelligence #LLM #DataScience