Course · Intermediate
Tool Use with LLMs in Python
Once you can prompt an LLM, the next challenge is making those interactions reliable enough to use in real systems. You need outputs you can validate and work with programmatically, ways to extend what the model can do through tools, and patterns that handle the inevitable edge cases. This course teaches you the core patterns that turn experimental prompts into dependable building blocks—from structured outputs with automatic repair to multi-step tool execution to reusable tool servers.
- Intermediate friendly
- 6 hrs
- 3 lessons
- Premium
Course overview
Learn to build reliable LLM systems with structured outputs, function calling, and tool integration. Move beyond basic prompting to create maintainable workflows using validation, agentic loops, and the Model Context Protocol.
What's inside
3 lessons
- 01 120 minAdvanced Prompting Patterns
Learn to build production-ready LLM systems with structured outputs, validation, repair loops, prompt composition, multi-step pipelines, and testing. Transform unreliable demo prompts into maintainable, versioned infrastructure that generates consistent JSON.
- 02 120 minTool Integration with MCP
Learn to convert function calling tools into reusable MCP servers. Build a centralized product assistant that exposes inventory lookup and bulk pricing tools through the Model Context Protocol, making them accessible to any MCP-compatible application without code duplication.
- 03 120 minFunction Calling
Learn how LLMs request structured tool calls instead of executing code directly. Build an agentic loop that handles multi-step queries, implements error recovery, and safely executes tools with proper validation.
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