OpenAI API Guide

If you are integrating OpenAI today, start with the Responses API. It is the default path for new builds, and it gives you one cleaner surface for text generation, image input, tool use, and multi-turn state.

#The shortest useful mental model

For most new projects, the API surface breaks down like this:

• Responses API: the default entry point
• Chat Completions: mainly for older systems and compatibility
• Embeddings: for retrieval, clustering, and recommendation
• Assistants: mostly relevant when you are reading or maintaining legacy implementations

That framing saves a lot of time because it stops you from learning the stack backward through outdated tutorials.

#Who this guide is for

• backend or full-stack engineers shipping the first real OpenAI integration
• teams moving from prototype code to something operational
• technical leads choosing the default API path for a product

#Why Responses API should come first

The case for Responses API is not just that it is newer. It is that it brings the main capabilities behind one more coherent interface:

• text generation
• image input
• tool use
• function calling
• multi-turn interactions

That leads to a cleaner implementation model and fewer branching decisions early on.

#What to get working in phase one

Most teams only need four things at the start:

1. load the API key safely
2. make the first successful responses.create call
3. handle errors and rate limits properly
4. add streaming, tools, or image input only when the product actually needs them

That matters more than touching every advanced feature on day one.

#Recommended reading order

1. Quickstart
2. Authentication
3. Chat basics
4. Streaming
5. Function calling
6. Vision

#Official sources

• Quickstart: https://platform.openai.com/docs/quickstart↗
• Migrate to Responses: https://platform.openai.com/docs/guides/migrate-to-responses↗
• Models overview: https://platform.openai.com/docs/models↗