In the age of AI, large language models (LLMs) like OpenAI's GPT let you build chatbots and other AI apps that can answer questions with natural language. But these general-purpose LLMs lack the specialized context relevant to your use case. Retrieval-Augmented Generation (RAG) is a technique for surpassing the capabilities of generic AI apps by pulling domain-, app-, and/or user-specific data into the generation pipeline.

Convex supports several different strategies for building tailored AI experiences by using your Convex data for RAG. Whether using Convex's built-in vector database or popular tools like Langchain, you can build bespoke AI interfaces that augment standard LLM outputs with additional context retrieved from your Convex data.