Retrieval-Augmented Generation (RAG)
A technique that lets AI models search your documents or databases before answering, combining real-time data retrieval with text generation.
What is Retrieval-Augmented Generation (RAG)?
Retrieval-Augmented Generation (RAG) is a technique that connects AI models to external data sources so they can search and retrieve relevant information before generating responses.
Instead of relying only on training data, RAG systems query your documents, databases, or knowledge bases in real-time, then use that retrieved context to ground their answers in your actual data.
Most builders use RAG to build AI assistants that can answer questions about company docs, customer data, or technical documentation. The system converts your documents into searchable embeddings, finds the most relevant chunks when someone asks a question, and feeds those chunks to the AI model for context.
Popular RAG tools include Pinecone, Weaviate, and LangChain for implementation. Most vector databases offer free tiers to get started.
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How Vibe Coders Use Retrieval-Augmented Generation (RAG)
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