TLDR/Teaser: Retrieval Augmented Generation (RAG) is the go-to method for turning LLMs into domain experts, but it’s not without its pitfalls. Enter Agentic RAG—a smarter, more dynamic approach that solves common RAG frustrations like irrelevant search results and ignored context. Learn how to implement it and turn your LLM into a true expert.
Why Agentic RAG Matters
If you’ve ever tried to implement RAG, you know the pain points: the wrong text gets retrieved, the LLM ignores the context you provided, and the whole system feels like it’s held together with duct tape. These issues aren’t just annoying—they can derail your entire project. That’s why Agentic RAG is a game-changer. It gives your LLM the ability to reason about the data it retrieves, making it smarter, more accurate, and less frustrating to work with.
What is Agentic RAG?
At its core, RAG is about augmenting an LLM’s responses with external knowledge. Traditional RAG works like this: you split your documents into chunks, turn those chunks into vectors, and store them in a vector database. When a user query comes in, it’s matched to the most relevant chunks, which are then fed to the LLM as context.
But here’s the catch: traditional RAG is a one-shot process. The LLM gets a single set of context and has to work with it, even if it’s incomplete or irrelevant. Agentic RAG flips the script by turning RAG into a set of tools that the LLM can use dynamically. Instead of a single retrieval, the LLM can reason about where to look, how to refine its search, and even pull additional context if needed.
How Agentic RAG Works
Agentic RAG transforms RAG from a static process into a dynamic, intelligent system. Here’s how it works:
- Dynamic Retrieval: The LLM can decide which parts of the knowledge base to query, based on the user’s question.
- Multiple Tools: Instead of just retrieving chunks, the LLM can use tools like URL listing, page-specific searches, and metadata filtering to refine its results.
- Iterative Reasoning: If the initial retrieval isn’t enough, the LLM can go back and search again, pulling in more context as needed.
This approach makes the LLM more like a detective than a librarian—it doesn’t just fetch what you ask for; it figures out what you really need.
Real-World Example: Building a Pydantic AI Expert
Let’s say you’re building an LLM to answer questions about Pydantic AI documentation. With traditional RAG, you might scrape the docs, chunk them, and store them in a vector database. But when a user asks for a specific example, like the “weather agent,” the LLM might pull irrelevant chunks or miss the mark entirely.
With Agentic RAG, you can give the LLM tools to:
- List all documentation pages.
- Retrieve the full content of a specific page.
- Use metadata (like titles and summaries) to refine its search.
In this setup, the LLM can reason about where to look for the weather agent example, pull the right page, and deliver a complete, accurate response.
Try It Yourself: Implementing Agentic RAG
Ready to give Agentic RAG a spin? Here’s how you can get started:
- Set Up Your Knowledge Base: Use a tool like Crawl4AI to scrape your documentation and store it in a vector database (Supabase is a great choice).
- Build Your Agent: Use Pydantic AI to create an agent with tools for dynamic retrieval, URL listing, and page-specific searches.
- Test and Iterate: Start with basic RAG, then add Agentic tools to handle more complex queries.
By giving your LLM the ability to reason about its knowledge base, you’ll get more accurate, consistent results—and fewer headaches.
Conclusion
Agentic RAG isn’t just a technical upgrade—it’s a mindset shift. Instead of treating your LLM as a passive tool, you’re empowering it to think, reason, and explore. Whether you’re building a Pydantic AI expert or a custom solution for your business, Agentic RAG can help you unlock the full potential of your LLM. So go ahead—give it a try, and see how much smarter your AI can get.
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