OpenAI's Cookbook: What Developers Are Actually Finding

Logan Kilpatrick from OpenAI called the Cookbook "one of the most underrated and underused developer resources available today." That statement caught my attention. 67,000 GitHub stars suggest plenty of people know about it. But underused? That's interesting.

The OpenAI Cookbook is a collection of code examples and guides for building with the OpenAI API. Think of it as the unofficial manual that shows you how things actually work. It includes working Python code examples, though the concepts apply to any language. You need an API key to run the examples, but browsing is free.

Here's what surprised me. The site has over 200 cookbook entries, covering everything from basic chat completions to building full coding agents with GPT-5. Recent additions include self-evolving agents, realtime voice frameworks, and guides for the new reasoning models. But finding the right example? That's where things get messy.

Why developers keep coming back

Developers credit the Cookbook as essential for understanding how to interact with the API correctly. The official docs tell you what endpoints exist. The Cookbook shows you how to use them without breaking things.

I talked to a few people who use it regularly. Most said the same thing. The docs are great for reference. The Cookbook is where you go when you need to actually build something.

Key resources include guides on improving LLM reliability, embedding long texts, processing Whisper transcripts, and implementing function calling. These aren't basic tutorials. They're solutions to problems you hit after your first prototype fails.

The Cookbook saves you from reinventing solutions that already exist.

One developer mentioned using it to build a logging service. Started with the basics, found the exact pattern they needed, shipped it in a week. Without it, they estimated two or three weeks of trial and error.

The function calling problem

A developer posted about struggling with function calling examples, noting inconsistencies in how to handle tool calls and frustration that basic patterns seemed deliberately omitted. This hits on something bigger.

The Cookbook assumes you know what you're looking for. If you're trying to learn from scratch, you'll get lost. Examples jump between difficulty levels. Some show simple patterns. Others assume you understand agentic workflows and multi-tool orchestration.

And the explanations? Sometimes they're there. Sometimes they're not.

Here's what happens. You find an example for building a voice assistant. Great. But it uses the Agents SDK, web search, and MCP servers. You don't know what MCP is. The example doesn't explain. You go searching for another cookbook entry about MCP. Now you're reading three different guides trying to understand one example.

The community forum reflects this. People asking basic questions about patterns that should be straightforward. Others jumping in to fill gaps. It works, but it's inefficient.

The real value isn't where you think

Most people use the Cookbook for code snippets. Fair enough. But the actual value is seeing patterns.

How do you structure a RAG system? There are six different examples showing different approaches. Pinecone, Chroma, Redis, Qdrant, Weaviate. Each one reveals trade-offs the docs never mention.

Want to fine-tune a model? There are examples covering direct preference optimization, reinforcement fine-tuning, and supervised fine-tuning. You can compare approaches side by side.

The examples show you what production code looks like. Not tutorial code. Not hello world. Real implementations with error handling and edge cases.

That weird GitHub decision

The Cookbook is described as a living, community-curated reference that shows "how to do X with the API" rather than just reprinting documentation. The repository accepts community contributions. Good pull requests get merged.

But here's the weird part. Most people don't contribute. They fork it, make changes for their projects, and never submit them back.

Why? Probably because the contribution guidelines are vague. What makes a good cookbook entry? Nobody really knows. The existing examples vary wildly in quality and depth.

Some are five lines of code with no explanation. Others are 300-line implementations with detailed commentary. There's no standard format. No clear editorial direction.

It's like a wiki that nobody wants to edit because they're not sure what belongs there.

What's actually missing

Developers have requested better organization by topics and difficulty levels to make navigation and learning easier. Right now, everything's just a chronological list. Most recent stuff at the top.

You can filter by tags. Audio. Functions. Embeddings. Vision. But tags don't tell you if something's for beginners or experts.

Want to learn embeddings from scratch? Good luck figuring out which of the 30 embedding examples to start with. They're all mixed together. Basic semantic search sits next to advanced hybrid search algorithms.

And there's no learning path. No "start here, then try this, then move to that." You're on your own.

The documentation paradox

The Cookbook has better examples than the official docs. But worse organization. The docs have better structure. But fewer real-world examples.

You need both. Copy the API reference from the docs. Open five Cookbook tabs. Piece together what you actually need.

Some developers note that certain Cookbook sections become outdated for advanced work as the API evolves. Models get updated. New features ship. Old examples break or use deprecated patterns. Sometimes they get fixed. Sometimes they don't.

The velocity of OpenAI's releases makes this worse. GPT-4 examples. GPT-4 Turbo examples. GPT-4o examples. GPT-5 examples. Which one should you actually use? The Cookbook doesn't always tell you.

Where this goes

The Cookbook will keep growing. More examples. More patterns. More notebooks covering new features like the Responses API and Agents SDK.

But it needs editing. Curation. Someone to decide what stays and what goes. What needs updating. What's redundant.

The resource includes examples for embedding visualization, dynamic image masking with DALLE, and implementing search and retrieval systems. That's valuable. But it's buried under everything else.

I still use it. Most developers do. You kind of have to if you're building with OpenAI. But i wish it was easier to navigate. Less overwhelming. More intentional about what it includes.

Maybe that's why it's underused despite all those stars. People know it exists. They just don't know where to start.