What is RAG (Retrieval-Augmented Generation)?

Retrieval-Augmented Generation (RAG) combines retrieval from a knowledge base with generative models to produce answers that are both fluent and grounded in your documents. RAG platforms and Conversation AI Agents use this pattern to build accurate assistants on top of company knowledge.

How RAG works — a short overview

1. Indexing: Documents are embedded using vector embeddings and stored in a vector database.
2. Retrieval: User queries are converted to embeddings and matched to relevant passages.
3. Generation: The selected passages are provided as context to a language model which generates a grounded response.

Why use a RAG SaaS or RAG platform?

• Accuracy: Answers are grounded in source documents.
• Freshness: You can update the knowledge base without retraining the model.
• Scalability: SaaS platforms manage indexing, embeddings, and vector storage for you.

Example: Building a Conversation AI Agent

A typical RAG-powered agent pipelines user input through semantic search, then prompts a generative model with the top passages and conversation history to produce helpful answers and suggested actions.

Further reading & resources

Check our features and use cases pages for practical examples and demos.