Retrieval-Augmented Generation (RAG): Stop Your AI from Hallucinating

Short summary

RAG (Retrieval-Augmented Generation) grounds AI answers in real documents, reducing hallucinations by searching a knowledge base before generating responses. The article includes LangChain code examples using FAISS vector stores, chunking strategies, hybrid search, and reranking techniques. It covers common mistakes like poor chunking and outdated documents, plus tools like Pinecone, Weaviate, and Milvus for production deployments.

• •RAG retrieves relevant documents before the LLM generates answers, reducing hallucinations
• •Code examples show LangChain + FAISS pipeline with chunking and similarity thresholds
• •Common pitfalls: poor chunking, stale docs, bad embeddings — each with practical fixes

Generated with AI, which can make mistakes.