OpenAI Cookbook vs Prompt Engineering Guide
Which should you use? A side-by-side look at what each one does, who it suits, what it costs, how hard it is to set up, and where each falls short.
Open source · MIT
Example code and guides for common tasks with the OpenAI API.
Open source · MIT
A free online guide, papers list, and notebooks for learning how to prompt and build with language models.
Our verdict
Both are free, MIT-licensed learning resources, but they teach different things. The OpenAI Cookbook is a set of runnable Python notebooks showing how to do specific tasks with the OpenAI API, so it suits people who want hands-on code for that API. The Prompt Engineering Guide is a browsable website of lessons, techniques, and papers on writing good prompts for language models in general, so it suits people who want to understand the ideas first.
Pick the OpenAI Cookbook if you already have or plan to get an OpenAI API key and want working Python code to read, run, and adapt.
Pick the Prompt Engineering Guide if you want structured reading on how prompting works and don't need code for one specific provider.
| Key difference | OpenAI Cookbook | Prompt Engineering Guide |
|---|---|---|
| Main format | Jupyter notebooks with runnable Python code examples | Website of guides, techniques, papers, and notebooks |
| Setup | Needs an OpenAI account and API key; usage may cost money | Read it online; nothing to install or sign up for |
| Best at | Showing how to build specific tasks with the OpenAI API | Explaining prompting techniques and related topics like RAG and agents |
| Language focus | Mostly Python; other languages need translation | Language-agnostic concepts, not tied to one provider |
| Maintenance | Code updated in the last day; official OpenAI examples | Last code update 8 months ago; review schedule not stated |
Written with AI from both products' catalogue entries · updated 2026-10-29
Who each one is for
Choose OpenAI Cookbook if you need…
- Developers who are new to the OpenAI API and want working examples
- Python programmers who want to see API usage patterns in notebook form
- Students or self-learners exploring how to build with language models
- Teams looking for reference code to adapt for their own projects
Choose Prompt Engineering Guide if you need…
- People who are new to prompting and want structured lessons with examples
- Developers who want an overview of prompting techniques like chain-of-thought and ReAct
- Researchers looking for a curated list of prompt engineering papers
- Teachers or students building a course around language model basics
Side-by-side
| OpenAI Cookbook | Prompt Engineering Guide | |
|---|---|---|
| Type | Learning resource | Learning resource |
| Price | Free and open source | Free and open source (hosted plans: free) |
| Licence | MIT | MIT |
| GitHub stars | 76.9k | 79.8k |
| Setup difficulty | Beginner | Beginner |
| How you get it | Clone and build from source | Read it online |
| Platforms | — | Web |
| Works with | — | — |
| Main language | Jupyter Notebook | MDX |
| Last code update | 5 days ago | 8 months ago |
What each one is
OpenAI Cookbook
The OpenAI Cookbook is a collection of example code and written guides showing how to accomplish common tasks with the OpenAI API. Most examples are Jupyter notebooks written in Python, though the README notes the ideas can be applied in other languages. It is mainly useful as a set of working references you can read, run, and adapt.
Prompt Engineering Guide
This is a collection of guides, research papers, lectures, notebooks and reference links about prompt engineering, the practice of writing and structuring inputs to get better results from large language models. The README also covers related topics such as context engineering, retrieval augmented generation (RAG), and AI agents. It is mainly a reading resource published as a website at promptingguide.ai, not a software program you install.
Best for
OpenAI Cookbook
- Developers who are new to the OpenAI API and want working examples
- Python programmers who want to see API usage patterns in notebook form
- Students or self-learners exploring how to build with language models
- Teams looking for reference code to adapt for their own projects
Prompt Engineering Guide
- People who are new to prompting and want structured lessons with examples
- Developers who want an overview of prompting techniques like chain-of-thought and ReAct
- Researchers looking for a curated list of prompt engineering papers
- Teachers or students building a course around language model basics
Strengths
OpenAI Cookbook
- Official examples maintained under the OpenAI organization
- Organized as runnable notebooks that show code and explanation together
- Released under the permissive MIT license
Prompt Engineering Guide
- Covers a broad range of topics, from beginner basics to techniques like RAG and agents
- Organized into clear sections for guides, techniques, papers, and notebooks
- Free to read online and the repository is MIT licensed
Not ideal for
OpenAI Cookbook
- People without an OpenAI account or API key, since the examples require one (the README says a free account can be created)
- Non-programmers looking for a ready-made app, because this is a set of code examples rather than a finished product
- Developers who need a non-OpenAI model provider, since the material is specific to the OpenAI API
Prompt Engineering Guide
- Anyone looking for a software tool to install, since the README describes a guide and resource collection rather than an application
- Readers who need up-to-date model-specific details, because the README does not say how often the content is reviewed and some model pages may age quickly
- People who want a hands-on, paid course; the README promotes DAIR.AI Academy courses separately and those are not part of this repository
Trade-offs
OpenAI Cookbook
- Examples depend on an OpenAI account and API key, which may incur usage costs
- Most code is in Python, so other-language users must translate the concepts
- The README is short and does not describe a version schedule or release history
Prompt Engineering Guide
- The README is a list of links, so the depth of each topic depends on the linked web pages, which this analysis did not review
- Some README content is promotional, including sponsorship and course discounts
- The README does not state how often the material is updated
Getting started
OpenAI Cookbook
Clone the repository
git clone https://github.com/openai/openai-cookbook.gitPrompt Engineering Guide
Clone the repository to read the source files locally
git clone https://github.com/dair-ai/Prompt-Engineering-GuideFrequently asked questions
Should I use OpenAI Cookbook or Prompt Engineering Guide?
Both are free, MIT-licensed learning resources, but they teach different things. The OpenAI Cookbook is a set of runnable Python notebooks showing how to do specific tasks with the OpenAI API, so it suits people who want hands-on code for that API. The Prompt Engineering Guide is a browsable website of lessons, techniques, and papers on writing good prompts for language models in general, so it suits people who want to understand the ideas first. Pick the OpenAI Cookbook if you already have or plan to get an OpenAI API key and want working Python code to read, run, and adapt. Pick the Prompt Engineering Guide if you want structured reading on how prompting works and don't need code for one specific provider.
What is the difference between OpenAI Cookbook and Prompt Engineering Guide?
OpenAI Cookbook: Example code and guides for common tasks with the OpenAI API. Prompt Engineering Guide: A free online guide, papers list, and notebooks for learning how to prompt and build with language models. OpenAI Cookbook suits developers who are new to the openai api and want working examples, while Prompt Engineering Guide suits people who are new to prompting and want structured lessons with examples.
Are OpenAI Cookbook and Prompt Engineering Guide free?
OpenAI Cookbook: Free and open source. Prompt Engineering Guide: Free and open source (hosted plans: free).
Is OpenAI Cookbook or Prompt Engineering Guide more popular?
Prompt Engineering Guide has more GitHub stars (79.8k) than OpenAI Cookbook (76.9k). Popularity isn't the same as fit — check which one matches your use case above.
What are other alternatives to OpenAI Cookbook and Prompt Engineering Guide?
Other options people compare them with include LangChain, LlamaIndex, Semantic Kernel, Learn Prompting.
Keep comparing
Built from our catalogue entries for both products: open-source details come from each project's README (AI-assisted, refreshed weekly) and GitHub data; AI-tool details are editorial. Spotted something wrong? Let us know from either product's page.