Dependency Injection, Contextual Relevancy, and Evaluation — The Overlooked Essentials for Building Agentic RAG Systems
We’ve talked about Retrieval-augmented generation (RAG) a lot in this newsletter and on our channel. It has become a popular technique for enhancing large language models with access to external knowledge. However, basic RAG approaches have limitations.
In this post, we’ll explore how to build more intelligent AI tools using agentic RAG and the Pydantic AI framework. Our new video will give a detailed walkthrough of this approach.
The Limitations of Basic RAG
Standard RAG typically involves retrieving relevant passages from a knowledge base and providing them as context to an LLM. While this can work well for many use cases, it falls short when:
- The knowledge base doesn’t contain relevant information
- The query requires synthesizing information from multiple sources
- The system needs to make decisions about how to handle different types of queries
This is where agentic RAG comes in.
As we wrap up this year, I want to take a moment to thank you for your support and readership for my blog. Your engagement has made this journey incredibly rewarding, and I’m excited to bring you even more…
