What is a RAG AI agent?

Summary

• A RAG (retrieval-augmented generation) AI agent combines an LLM with real-time retrieval: it fetches relevant documents at query time and uses them as context, so answers are grounded in verified, current data — not only the model's training.
• The flow has three stages: retrieval (query a knowledge base — vector store, keyword search, or SQL), augmentation (combine retrieved context with the request in a prompt template), and generation (the LLM answers grounded in that context).
• Grounding in retrieved, authoritative data reduces hallucinations and lets an agent cite its sources.
• On Databricks, Agent Bricks Knowledge Assistant is a managed RAG solution that chunks and vectorizes documents, selects embedding and LLM models, tunes retrieval, and deploys a production API with citations.
• Databricks AI Search powers retrieval with hybrid dense-and-sparse search and auto-sync from Unity Catalog tables; documents, embeddings, index, retrieval, and generation are governed end-to-end and traced with MLflow.

What is a RAG AI agent?

Retrieval-augmented generation (RAG) is a technique that combines a large language model (LLM) with real-time data retrieval to produce accurate, up-to-date, and contextually relevant responses. A RAG AI agent retrieves relevant documents at query time and uses them as context for generation, so it answers from your verified data — not only from what the model learned during training. Grounding answers in retrieved, authoritative data reduces hallucinations and lets the agent cite its sources. See the RAG glossary and what retrieval-augmented generation is on Databricks.

Why Databricks Agent Bricks for RAG AI agents

• How RAG works. A RAG flow has three core stages: retrieval, where the request queries an external knowledge base such as a vector store, keyword search, or SQL database; augmentation, where the retrieved data is combined with the request in a prompt template with instructions; and generation, where the LLM produces a response grounded in the retrieved context. Optional query preprocessing and post-processing (citations, business logic, content moderation) refine the chain. See the RAG inference-chain fundamentals.
• Managed RAG with Knowledge Assistant. Agent Bricks Knowledge Assistant is a managed, production-ready RAG solution that turns enterprise documents into accurate, cited answers: it chunks and vectorizes documents, selects embedding and LLM models, tunes retrieval, deploys a production-ready API, and monitors performance. See Agent Bricks Knowledge Assistant, now generally available.
• Instructed Retriever architecture. Knowledge Assistant is powered by Instructed Retriever, which flows system specifications — instructions, examples, and knowledge-source schemas — through each component, so it can translate a request into structured search queries with the right column filters and honor complex instructions and metadata constraints. See Instructed Retriever.
• Retrieval with Databricks AI Search. AI Search provides hybrid dense-and-sparse retrieval, automatically refreshes when Unity Catalog source tables change, and lets you use Databricks-managed embedding endpoints or bring your own — all governed by Unity Catalog permissions.
• Governed and observable end-to-end. Documents, embeddings, and the vector index are governed under Unity Catalog with consistent permissions; MLflow tracing captures each retrieval and generation step for debuggability; LLM judges score answer quality; and guardrails enforce permission checks and content moderation. See categories of LLM apps.

Getting started

• Read the RAG glossary.
• Learn what retrieval-augmented generation is on Databricks.
• Follow the RAG inference-chain fundamentals.
• See Agent Bricks Knowledge Assistant, now generally available.

FAQs

What is a RAG AI agent?

A RAG (retrieval-augmented generation) AI agent is an agent that retrieves relevant documents from a knowledge base at query time and uses them as context, so an LLM generates answers grounded in verified, current data.

How does RAG work?

In three stages: retrieval (query a knowledge base), augmentation (combine the retrieved context with the request in a prompt), and generation (the LLM answers grounded in that context), often with optional query preprocessing and post-processing.

Why ground an agent's answers with RAG rather than a model on its own?

RAG grounds answers in retrieved, authoritative data, which reduces hallucinations, keeps responses current, and lets the agent cite its sources — without retraining the model.

How does Databricks build RAG agents?

With Agent Bricks Knowledge Assistant for managed RAG and Databricks AI Search for retrieval, governed end-to-end by Unity Catalog and traced with MLflow.