Function Calling with LLMs: Production Patterns
How function calling really works under the hood, the schema design that survives contact with users, and the failure modes to plan for.
5 posts · page 1 of 1
How function calling really works under the hood, the schema design that survives contact with users, and the failure modes to plan for.
How to design tool schemas that LLMs actually call correctly, with naming, description, and parameter patterns that survive real users and adversarial inputs.
How to write prompts and tool definitions that make function calling reliable. Covers schemas, descriptions, examples, error handling, and patterns for multi-tool agents.
How to get reliable JSON out of LLMs using tool use, JSON mode, and grammar-constrained decoding, with patterns that work in production.
Practical patterns for building AI agents that use tools well: tool definitions, loops, planning, parallel calls, error handling, and how to keep agents from going off the rails.