TLDR/Teaser: Retrieval Augmented Generation (RAG) is the go-to method for supercharging LLMs with external knowledge, but it’s not without its pitfalls. Enter Agentic RAG—a smarter, more dynamic approach that lets your LLM reason about its knowledge base instead of blindly retrieving chunks. In this post, I’ll show you how to implement Agentic RAG, step by step, and turn your LLM into a true expert.
Why Agentic RAG Matters
If you’ve ever tried implementing RAG, you’ve probably run into the same frustrations: irrelevant chunks being retrieved, the LLM ignoring your carefully curated context, or the whole system collapsing under the weight of its own complexity. These issues aren’t just annoying—they’re deal-breakers for building reliable, production-ready AI systems.
Agentic RAG solves these problems by giving your LLM the ability to reason about its knowledge base. Instead of a one-shot retrieval, the LLM can iteratively refine its search, explore different data sources, and even decide which tools to use. The result? More accurate, context-aware responses that don’t leave you questioning your life choices.
What is Agentic RAG?
At its core, RAG works by retrieving relevant chunks of information from a knowledge base and feeding them into the LLM as context. Traditional RAG is a one-shot process: the LLM gets a single set of chunks and has to work with what it’s given. Agentic RAG, on the other hand, turns RAG into a tool that the LLM can use dynamically.
Think of it like this: with traditional RAG, your LLM is handed a single book and told to find the answer. With Agentic RAG, your LLM has access to an entire library, and it can decide which books to pull off the shelf, which chapters to read, and even when to ask for more information.
How Agentic RAG Works
Agentic RAG isn’t just a theoretical concept—it’s something you can implement today. Here’s how it works in practice:
- Step 1: Build Your Knowledge Base – Start by scraping and chunking your data (e.g., documentation, articles, or product info). Store these chunks in a vector database like Supabase or Weaviate.
- Step 2: Add Tools for Dynamic Retrieval – Instead of just retrieving chunks, give your LLM tools to list, filter, and explore the knowledge base. For example, it can list all URLs in the database or fetch the full content of a specific page.
- Step 3: Let the LLM Reason – With these tools, the LLM can decide how to retrieve information. If the first chunk isn’t enough, it can search again, explore a different page, or even combine multiple sources.
Stories/Examples
Let’s say you’re building an AI agent to answer questions about the Pydantic AI documentation. With traditional RAG, you might ask for the “weather agent example” and get a half-baked response because the LLM only retrieved a small, irrelevant chunk. With Agentic RAG, the LLM can:
- List all documentation pages.
- Identify the page with the weather agent example.
- Fetch the full content of that page.
- Return a complete, accurate response.
This approach not only improves accuracy but also makes the system more robust and adaptable to complex queries.
Try It Yourself
Ready to implement Agentic RAG? Here’s how to get started:
- Scrape Your Data – Use tools like Crawl4AI to scrape and chunk your documentation or other knowledge sources.
- Set Up Your Database – Use Supabase or Weaviate to store your chunks and embeddings. Don’t forget to add metadata (e.g., source, title, summary) for better filtering.
- Build Your Agent – Use Pydantic AI to create an agent with tools for dynamic retrieval. Start with basic RAG, then add tools for listing and fetching specific pages.
- Test and Iterate – Try out your agent with real-world queries. If it struggles, add more tools or refine your chunking strategy.
For a hands-on example, check out the GitHub repository where I’ve implemented an Agentic RAG solution for the Pydantic AI docs. You can clone it, tweak it, and make it your own.
Final Thoughts
Agentic RAG isn’t just a fancy buzzword—it’s a practical solution to the real-world challenges of working with RAG. By giving your LLM the ability to reason about its knowledge base, you can build smarter, more reliable AI systems that don’t leave you pulling your hair out. So, what are you waiting for? Dive in, experiment, and let me know how it goes!
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