A technique that enhances LLM responses by retrieving relevant context from external knowledge bases.
Retrieval-Augmented Generation (RAG) addresses the fundamental limitation of Large Language Models: they can only respond based on their training data, which becomes stale over time. RAG solves this by adding a retrieval step before generation. When a user asks a question, the system first searches a vector database of your proprietary documents, retrieves the most relevant chunks, and injects them into the LLM prompt as context. This produces answers that are grounded in your actual data, dramatically reducing hallucinations.
Your documents are split into chunks, converted to vector embeddings using a model like OpenAI Ada, and stored in a vector database.
The user's question is converted into a vector using the same embedding model.
The vector database performs a similarity search to find the most relevant document chunks.
The retrieved chunks are injected into the LLM prompt as context, and the model generates a grounded response.
Employees ask questions in natural language and get answers sourced from internal documentation, Confluence, and Slack.
A chatbot that answers product questions using your actual product docs, reducing hallucination risk.
Lawyers query case law databases and receive cited, contextual answers from relevant precedents.
Knowing the definition is step one. Building it into your product is step two. That's where we come in.