OpenAI Cookbook
by Various
Examples and guides for using the OpenAI API
Apps
OpenAI Cookbook
Added 1 June 2026
Overview
A collection of Jupyter notebooks and code examples demonstrating how to use the OpenAI API for common tasks. Covers prompt engineering, embeddings, fine-tuning, and integration patterns with practical, runnable code samples.
Best for
Best for
Developers new to OpenAI API who learn by reading and running code
Use cases
- Learning OpenAI API fundamentals through working examples
- Copy-paste starter code for embeddings and semantic search
- Understanding prompt engineering best practices
Notes
A collection of Jupyter notebooks and code examples demonstrating how to use the OpenAI API for common tasks. Covers prompt engineering, embeddings, fine-tuning, and integration patterns with practical, runnable code samples.
73,919 stars on GitHub. Last updated 2026-06-01. Licensed MIT.
Use cases
- Learning OpenAI API fundamentals through working examples
- Copy-paste starter code for embeddings and semantic search
- Understanding prompt engineering best practices
Pros
- Maintained by OpenAI with current API patterns
- Executable Jupyter notebooks reduce setup friction
- Covers both basic and advanced use cases
Cons
- Examples may lag behind latest API features
- No structured curriculum, requires self-directed navigation
- Notebook format less suitable for production deployment
Indexed from awesome-generative-ai and enriched against its public facts.
Pros
- Maintained by OpenAI with current API patterns
- Executable Jupyter notebooks reduce setup friction
- Covers both basic and advanced use cases
Cons
- Examples may lag behind latest API features
- No structured curriculum, requires self-directed navigation
- Notebook format less suitable for production deployment
Open-source & AI alternatives
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