An AI architecture that grounds LLM responses in external documents, reducing hallucinations and enabling use of proprietary data.
RAG works by embedding documents into a vector database, then retrieving the most relevant chunks at query time and injecting them into the LLM's context window. This allows the model to answer questions based on your company's internal knowledge base, recent documents, or real-time data — without expensive fine-tuning. It is the dominant architecture for enterprise AI applications.
Stop trying to fine-tune open-source models on your company data. Retrieval-Augmented Generation (RAG) is cheaper, faster, and infinitely more reliable.
Most founders who try AI agents hit the same wall: it works in the demo, breaks in production. Here's why — and how to build agents that hold up.
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