Examples & learning
openai/openai-cookbook
The OpenAI Cookbook is a collection of official examples for using OpenAI models and APIs. It is a practical reference for learning request patterns, tools, retrieval, agents and evaluation. Individual notebooks demonstrate a technique; they are not complete production applications.
THE PRACTICAL EXPLANATION
The OpenAI Cookbook is a collection of official examples for using OpenAI models and APIs. It is a practical reference for learning request patterns, tools, retrieval, agents and evaluation. Individual notebooks demonstrate a technique; they are not complete production applications.
WHERE TO USE IT
Use it when learning a current API, comparing implementation patterns or creating a small proof of concept from an official example.
WHO MAY USE IT
Developers, data practitioners and technical product teams working with OpenAI APIs at any stage from first experiment to architecture review.
HOW TO USE IT
Find an example that matches the current API and date, run it unchanged, then reduce it to the part your use case needs. Replace sample data and credentials safely, add error handling and evaluate the pattern against real business cases before adopting it.
GETTING THE BEST RESULTS
WHY IT MAY BE USEFUL
Official examples and guides for the OpenAI API.
Best considered for: Working examples for models, tools, agents, retrieval and evaluation.
READ BEFORE YOU ADOPT IT
Examples teach patterns; production systems still need security, monitoring and cost controls.
Confirm the current licence, maintenance status, dependency risk, data path, model access, tool permissions and human approval points at the source. A public repository is inspectable raw material—not proof that a system is secure, supported or suitable for your production environment.