RAG (Retrieval-Augmented Generation)
An AI technique that retrieves relevant documents to provide context for LLM responses.
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Definition
Retrieval-Augmented Generation (RAG) is an AI technique that combines information retrieval with text generation. Instead of relying solely on an LLM's training data, RAG retrieves relevant documents from a knowledge base and provides them as context for generating responses.
The RAG pipeline works in three steps: 1) User query is converted to an embedding (vector), 2) Similar documents are retrieved from a vector database (Pinecone, Weaviate, Chroma), 3) Retrieved documents are included in the LLM prompt as context for generating an accurate response.
RAG solves several LLM limitations: it provides access to up-to-date information, reduces hallucinations by grounding responses in real documents, and enables domain-specific knowledge without fine-tuning the entire model.
Why It Matters for Founders
RAG is the most practical way to build AI applications that need accurate, domain-specific knowledge. Without RAG, LLMs are limited to their training data and prone to hallucination. With RAG, you can build AI assistants that accurately answer questions about your specific product, company, or domain.
For startups, RAG is often the first step in building an AI-powered product. It is simpler and cheaper than fine-tuning, can be updated in real-time (just update the knowledge base), and provides transparent sourcing (you can show which documents informed the response).
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Real-World Example
A customer support bot uses RAG: when asked "How do I cancel my subscription?", it retrieves the company's cancellation policy from a knowledge base and generates an accurate, specific answer.
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Frequently Asked Questions
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