Topic
Function Calling Fundamentals
27 posts on function calling fundamentals — part of ai agents & automation on the n4n AI blog.
Writing tool descriptions for reliable function calling
Learn how writing tool descriptions function calling reliably works: a step-by-step guide to schema design, imperative phrasing, and cross-model testing.
Tool use vs function calling: is it the same thing?
Clarifying the tool use vs function calling difference: a head-to-head comparison of capabilities, cost, latency, ergonomics, and ecosystem for engineers.
How function calling works under the hood in GPT-4o
A practitioner's analysis of how function calling works in GPT-4o internals, covering training, constrained decoding, failure modes, and schema design tradeoffs.
Function calling with Gemini, Claude, and GPT-4o compared
Engineer-focused comparison of function calling gemini claude gpt-4o compared: schema differences, cost, latency, ergonomics, limits, and verdict.
Function calling vs JSON mode: what's the difference?
A practical comparison of function calling vs JSON mode for LLM apps: capabilities, cost, latency, ergonomics, and which to use for your use case.
Function calling tutorial: from schema to executed result
Hands-on function calling tutorial: define JSON schemas, invoke a model via OpenAI-compatible API, execute the call locally, and return results to the model.
Function calling schemas: JSON Schema for LLM tools
A practical guide to designing JSON Schema for LLM function calling: tool definitions, parameter constraints, pitfalls, and provider compatibility.
Common function calling mistakes and how to fix them
A practitioner's guide to the common function calling mistakes fix path: schema design, validation, retries, error handling, and cross-model testing.
Claude tool use vs OpenAI function calling compared
A practitioner's head-to-head comparison of Claude tool use vs OpenAI function calling across capabilities, cost, latency, ergonomics, and limits.
Writing your first function calling API request
Hands-on tutorial for your first function calling API request using the OpenAI-compatible Chat Completions API, with runnable Python code and expected outputs.
Why LLMs hallucinate function call arguments
Analyze why LLMs invent invalid function call arguments, from schema drift to token prediction, and how engineers can enforce reliable tool use.
What is function calling in the OpenAI API?
Function calling in the OpenAI API lets models return structured JSON to trigger external code. Learn the wire format, gotchas, and a real example.
What function calling actually costs in tokens
Function calling token cost hides in repeated schema injection, multi-turn context, and output overhead. Learn how to measure and cut it in production agents.
Parallel function calling explained with examples
Parallel function calling lets LLMs request multiple tool invocations in one response. Learn how it works, see code examples, and avoid common misconceptions.
OpenAI function calling explained with a real example
A hands-on OpenAI function calling example explained step by step. Build a weather assistant with the API, including runnable code and expected outputs.
How to write JSON schemas for function calling
Learn how to write a json schema for function calling that models reliably follow, with step-by-step examples and verification tips for engineers.
How to route function calls across multiple LLMs
Step-by-step guide to route function calls across models with OpenAI-compatible APIs, fallback logic, and per-call routing directives for reliable agents.
How to debug failed function calls in production
A practitioner's step-by-step guide to debug failed function calls in production: capture logs, isolate model vs tool errors, replay, and enforce schemas.
How to chain multi-step function calls reliably
A practical guide to chaining function calls across multiple LLM steps without losing state or reliability, with runnable code patterns.
Function calling with Llama 3.3 70B: a tutorial
Step-by-step tutorial for implementing Llama 3.3 function calling with open-source models, including runnable Python code and expected outputs.
Function calling latency: GPT-4o vs Claude vs Gemini
A practitioner's analysis of function calling latency benchmark results across GPT-4o, Claude, and Gemini, separating model speed from API and parsing overhead.
Function calling 101: how LLM tool use actually works
Explains how does llm function calling work: the JSON schema contract, the inference and execution loop, and the misconceptions engineers hit building tools.
Structured outputs vs function calling explained
A pragmatic engineering comparison of structured outputs vs function calling across capabilities, cost, latency, ergonomics, ecosystem, and limits.
How function calling works in GPT-4o and Claude
Function calling lets LLMs like GPT-4o and Claude emit structured tool calls. Learn how function calling works, with code examples and common pitfalls.
Function calling vs JSON mode: when to use each
A pragmatic engineering comparison of function calling vs json mode across capabilities, cost, latency, ergonomics, and limits—with a clear verdict.
Claude tool use vs OpenAI function calling
A practitioner's head-to-head comparison of Claude tool use vs OpenAI function calling across capabilities, cost, latency, ergonomics, and limits.
A beginner's guide to LLM function calling
A practical LLM function calling guide for engineers: learn to define schemas, invoke tools, execute safely, and avoid common pitfalls with real code.
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