How to Connect an LLM to Python Functions (Tool Calling Tutorial)
In this video, Dataquest’s Director of Curriculum, Anna Strahl, walks through a practical walkthrough on LLM tool calling (function calling) in Python using the OpenAI API.
You’ll learn how to build a real AI product assistant that can reliably:
Look up real product details
Return accurate pricing information
Calculate bulk order discounts
Instead of letting the model guess, you’ll connect your LLM to external Python functions using the tool calling / function calling workflow supported by modern AI APIs.
This is a key skill for anyone building AI agents, chatbots with tools, or production-style assistants that need to interact with real systems like databases, pricing logic, or APIs.
By the end of this walkthrough, you’ll understand how function calling works conceptually and how to implement it step-by-step in code.
What You’ll Learn:
LLM Function Calling in Python: Register tools and let the model decide when to call them
Tool Schema Design: Write clear JSON schemas so the model uses functions correctly
Preventing Hallucinations: Force accurate responses through external data sources
Bulk Pricing Calculations: Use Python functions for reliable math and discounts
Recommended Prerequisites:
Prompting Large Language Models in Python → https://www.dataquest.io/course/promp...
Access the lesson: https://app.dataquest.io/m/3000058
Video chapters:
00:00:00 - Intro
00:03:12 - Function calling workflow overview
00:05:39 - Why LLMs hallucinate without tools
00:09:21 - Writing the get_product_info function
00:12:53 - Defining tools with JSON schema
00:17:51 - Connecting the model to tools
00:22:26 - Executing tool calls and returning results
00:35:55 - Adding bulk pricing with calculate_bulk_price
00:43:45 - Making tool execution dynamic (tool_functions mapping)
00:46:03 - Next Steps
00:48:10 - Q&A
#LLMFunctionCalling #PythonAI #AIAgents #OpenAIAPI #GenerativeAI