Retrieval-Augmented Generation (RAG): Building Knowledge-Grounded Chatbots
Understand RAG architecture to build chatbots that ground responses in external knowledge bases, reducing hallucinations and improving factual accuracy.
Large language models sometimes generate plausible but false statements (hallucinations). Retrieval-Augmented Generation (RAG) mitigates this by retrieving relevant external documents (knowledge bases, databases, documents) before generating responses. The pipeline: (1) User query is embedded into a vector space, (2) A retriever searches a vector database of documents to find topically relevant passages, (3) The retriever's results are concatenated with the original query as context, (4) An LLM generates a response grounded in the retrieved documents. RAG is transformative for customer service, internal documentation Q&A, and domain-specific systems where accuracy is critical. It also provides transparency—responses cite source documents, enabling users…
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