Agentic RAG
An advanced RAG pattern where an AI agent dynamically decides what to retrieve, how to refine queries, and when to search again based on the quality of initial results.
Why It Matters
Agentic RAG significantly outperforms basic RAG by adapting its retrieval strategy. It handles complex queries that require multi-step research.
Example
An agent that searches a knowledge base, evaluates the results, realizes they are insufficient, reformulates the query, searches again with different terms, and synthesizes the combined findings.
Think of it like...
Like a skilled researcher who does not just run one search but iteratively refines their approach based on what they find — adapting strategy to get the best answer.
Related Terms
Retrieval-Augmented Generation
A technique that enhances LLM outputs by first retrieving relevant information from external knowledge sources and then using that information as context for generation. RAG combines the power of search with the fluency of language models.
AI Agent
An AI system that can autonomously plan, reason, and take actions to accomplish goals. Unlike simple chatbots, agents can use tools, make decisions, execute multi-step workflows, and adapt their approach based on results.
Tool Use
The ability of an AI model to interact with external tools, APIs, and systems to accomplish tasks beyond text generation. Tools extend the model's capabilities to include search, calculation, code execution, and more.