Glossary
Retrieval-Augmented Generation (RAG)
A technique that grounds a language model in your own data at query time.
Definition
Retrieval-Augmented Generation (RAG) is an AI architecture that combines a large language model with a retrieval system. Instead of relying solely on the model's training data, RAG retrieves relevant information from your own documents, databases, or knowledge bases at query time, then uses the model to generate a response grounded in that retrieved content.
Why it matters
RAG is what turns a generic AI assistant into a tool that actually knows your business. Without it, language models give generic answers that ignore your proprietary context. With it, an AI assistant can answer questions about your internal procedures, your product catalog, or your contracts, accurately and with traceable sources.
Example
An employee asks the internal assistant about the returns policy. The system retrieves the current policy document and the model answers from it, citing the source, instead of guessing from its training data.
Our take
RAG pipelines on proprietary data are one of our most requested AI integration patterns. We build them end to end: document ingestion, embedding, vector search, and generation, all in production.
Relevant services
Relevant sectors
Related terms