Module 1 of LLM Zoomcamp is done! 🎉
I turned my original RAG pipeline into an Agent!
I spent these last few days diving deep into Agentic RAG. It's been fascinating to build it step by step. Every time I ask the LLM to learn about something new, I see how it naturally figures out which tools to use, when to search, and how many times to gather info before giving me a solid answer.
What exactly is Agentic RAG?
It’s like giving the AI a brain that can actually act. Instead of just retrieving from a fixed knowledge base, the model decides whether it needs external tools first, gathers what it needs, and then answers. It’s pretty interesting to understand how it actually works behind the scenes!
Why does this matter?
A few days ago I asked for a detailed guide on using the OpenAI Python library with the chat.completion API. The Local LLM called web search multiple times until it had enough context and built something useful from those pieces. Now that I am building these systems, I can finally understand why it does what it does.
💡 Insights from this week:
- Building a static pipeline is a great start, but to make something truly flexible, you need function or tool calling. It lets the LLM look at the question first and decide whether it needs to search a knowledge base before answering.
- I used to think "chunking" was just about breaking up text. Turns out it can reduce token input by 3x! 🤯
- You have to learn how to walk before you run. Starting small, understanding each component manually, and seeing how the pieces fit together… it felt slow at first but worth it. Now I’m able to accelerate with agent frameworks like toyaikit, LangChain, PydanticAI, or OpenAI Agents.
- There is definitely a learning curve with the API syntax. Between the new response API and chat completions, tool responses are structured differently and you have to adjust your code accordingly. Frustrating at times, but also a great way to learn!
Quick takeaway:
It is best to start simple, then add complexity only when needed. Sometimes an agent can burn tokens unnecessarily, so only add that layer if your problem really needs it!
Had a lot of fun with this module and I’m already curious about what’s next. If you’re interested in learning along, this is the full free course Alexey at the Data Talks Club: https://github.com/DataTalksClub/llm-zoomcamp/
Anyone else tinkering with LLM agents lately? What kind of projects are you exploring or trying out? Would love to hear where your journey is heading!
#ai #localai #llm #mastodon #fediverse #buildinpublic #linux #github #aiengineering #DataEngineering
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