What

    What is RAG? Retrieval-Augmented Generation Explained

    RAG is the bridge between powerful language models and your private data. It's the most deployed pattern in production AI.

    Quick Answer

    RAG (Retrieval-Augmented Generation) enhances LLM responses by first retrieving relevant documents from a knowledge base, then using them as context for generation. This gives the LLM access to current, domain-specific information without fine-tuning.

    Why RAG Matters

    LLMs have a knowledge cutoff and can hallucinate. RAG solves both problems by grounding responses in your actual data. It's cheaper and faster than fine-tuning.

    The RAG Pipeline

    Indexing: chunk documents → generate embeddings → store in vector DB. Retrieval: convert query to embedding → find similar chunks. Generation: pass retrieved chunks + query to LLM → get grounded answer.

    Use Cases

    • Enterprise knowledge bases and documentation search
    • Customer support with product-specific answers
    • Medical or legal research assistants
    • Internal company Q&A systems

    When Not to Use

    • General knowledge questions the LLM already handles well
    • Tasks requiring real-time data (use function calling instead)
    • When your documents are too short to benefit from retrieval

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