From RAG to Agents: Making Smart AI Assistants (LLM Zoomcamp bonus module)

​A hands-on workshop on turning retrieval-augmented generation into agentic AI flows - Alexey Grigorev

​Description:

​Retrieval-Augmented Generation (RAG) is a common approach for building AI assistants, but most implementations stop at a single search + answer pattern. In this practical workshop, we go a step further.

​You'll learn how to make RAG pipelines more agentic: enabling decision-making, tool use, multi-step reasoning, and real-time interaction with users. We’ll build step by step, from simple retrieval to agent-powered assistants capable of asking follow-up questions, running tool calls, and updating their own knowledge base.

​What we’ll cover:

• ​Build a basic RAG pipeline: Implement search + prompt + LLM logic using course FAQs

• ​Add agentic behavior

  • ​Let the assistant decide when to search or answer

  • ​Introduce state, reasoning, and multi-step flows

• ​Implement agentic search

  • ​Iterate over multiple search queries

  • ​Use search history and reasoning to refine results

• ​Use OpenAI function calling

  • ​Add tool support: search and FAQ updates

  • ​Maintain interaction history and responses

• ​Build a working assistant

  • ​Organize your logic with chat_assistant.py

  • ​Interact with a live, modular AI assistant

• ​Explore PydanticAI

  • ​Define tools with type hints and docstrings

  • ​Run your assistant in a fully typed agent framework

​What you’ll get:

​A working RAG-based AI assistant that can:

• ​Search FAQs

• ​Decide when to act

• ​Ask follow-up questions

• ​Add entries to its own knowledge base
  

​Everything runs in Jupyter with Python. You can follow along with GitHub Codespaces or use your own environment.

​About the Speaker: 

​Alexey Grigorev is the creator of DataTalks.Club and instructor of the popular Zoomcamp series. He designs and teaches practical AI/ML workflows used by thousands of learners globally. In this workshop, Alexey brings his hands-on teaching style to the emerging field of agentic LLMs, helping you build useful assistants from scratch.

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