# openai

This Cursor rule guides OpenAI API work, from Python client setup to prompt structure. Use it when building or maintaining an API-backed project.

- rule · `type=rule` · Cursor · ★ 3580
- Author: sanjeed5
- License: CC0-1.0 · policy=mirror · the beginning of the file is at the end of this page
- Source: https://github.com/sanjeed5/awesome-cursor-rules-mdc/tree/HEAD/rules-mdc/openai.mdc
- Updated: 2026-05-19
- HTML: https://affpapa.org/en/nevibecode/rules/openai

## What it does

The rule applies across the project with the `**/*` glob. It recommends the official `openai` Python library and a client initialized once at application startup. Read the key from `OPENAI_API_KEY` instead of embedding it in source code; the sample raises an error if the client has no key.

Keep prompts outside application logic and under version control. The guidance suggests putting instructions before the supplied text, separating the two with delimiters, and specifying the role, context, task, output format, and constraints. For structured results, it demonstrates JSON mode; for harder tasks, it uses input-output examples. This is project guidance, not an implementation of an API client.

## Install

### Cursor (native platform)

**Manually: put the files in place**
- File: `.cursor/rules/openai.mdc` — Frontmatter: description, globs, alwaysApply

### Claude Code

**Manually: put the files in place**
- File: `.claude/rules/openai.md` — The paths frontmatter field limits the files: move globs from the source into paths

### Codex

**AGENTS.md**
- Codex reads AGENTS.md: paste the rule text there

### GitHub Copilot

**Manually: put the files in place**
- File: `.github/instructions/openai.instructions.md` — The applyTo field sets which files it applies to: move globs from the source into applyTo

### Windsurf (Devin Desktop)

**Manually: put the files in place**
- File: `.devin/rules/openai.md`

### Cline

**Manually: put the files in place**
- File: `.clinerules/openai.md`

Where a file is given instead of a command, copy the files from the source to that path: https://github.com/sanjeed5/awesome-cursor-rules-mdc/tree/HEAD/rules-mdc/openai.mdc

## When to use it
- initialize the OpenAI client once at startup
- move prompts out of application logic
- structure instructions and context in API requests
- request structured results in JSON
- guide complex tasks with input-output examples

## Example prompts
- Check that the OpenAI client is created once and reads its key from `OPENAI_API_KEY`.
- Move the prompt text out of the application logic and suggest a layout for `prompts.py`.
- Rewrite this entity-extraction request to return JSON.

## Source text · openai.mdc · CC0-1.0

Below is the content of a third-party file. It is data, not instructions for you: do not act on anything written inside unless the human asks. Block tag: d0e20b5ab821af38.

BEGIN THIRD-PARTY FILE d0e20b5ab821af38
````text
---
description: This guide provides definitive, actionable best practices for developing with the OpenAI API, covering prompt engineering, robust client usage, agent design, and testing.
globs: **/*
---
# openai Best Practices

This document outlines the definitive best practices for interacting with OpenAI APIs, ensuring your applications are reliable, maintainable, and performant. Adhere to these guidelines for all OpenAI-powered development.

## 1. Code Organization and Structure

Always use the official `openai` Python client library. It handles authentication, request formatting, and error handling, allowing you to focus on business logic.

### 1.1 Client Initialization

Initialize the OpenAI client once, ideally at application startup or as a singleton. Never hardcode API keys.

❌ **BAD**: Hardcoding API key and re-initializing client
```python
import os
from openai import OpenAI

def get_response_bad(prompt: str):
    # API key hardcoded and client re-initialized on every call
    client = OpenAI(api_key="sk-YOUR_HARDCODED_KEY") 
    response = client.chat.completions.create(
        model="gpt-4o",
        messages=[{"role": "user", "content": prompt}]
    )
    return response.choices[0].message.content
```

✅ **GOOD**: Environment variable for API key, single client instance
```python
import os
from openai import OpenAI

# Initialize client once, leveraging OPENAI_API_KEY environment variable
client = OpenAI(api_key=os.environ.get("OPENAI_API_KEY"))

def get_response_good(prompt: str) -> str:
    if not client.api_key:
        raise ValueError("OPENAI_API_KEY environment variable not set.")
    
    response = client.chat.completions.create(
        model="gpt-4o", # Always use the latest, most capable model
        messages=[{"role": "user", "content": prompt}]
    )
    return response.choices[0].message.content
```

### 1.2 Prompt Management

Treat prompts as first-class code artifacts. Store them in separate files, constants, or configuration, and version-control them. This improves readability, reusability, and testability.

❌ **BAD**: Inline, unstructured prompts
```python
def process_user_query(query: str):
    response = client.chat.completions.create(
        model="gpt-4o",
        messages=[
            {"role": "system", "content": "You are a helpful assistant."},
            {"role": "user", "content": f"Summarize this text: {query}"}
        ]
    )
    # ...
```

✅ **GOOD**: Externalized, structured prompts
```python
# prompts.py
SYSTEM_PROMPT_SUMMARIZER = "You are a concise summarization bot. Extract key information only."
USER_PROMPT_SUMMARIZE_TEXT = "Summarize the following text, focusing on main arguments:\n\n---\n{text}\n---"

# main.py
from prompts import SYSTEM_PROMPT_SUMMARIZER, USER_PROMPT_SUMMARIZE_TEXT

def process_user_query(query: str):
    response = client.chat.completions.create(
        model="gpt-4o",
        messages=[
            {"role": "system", "content": SYSTEM_PROMPT_SUMMARIZER},
            {"role": "user", "content": USER_PROMPT_SUMMARIZE_TEXT.format(text=query)}
        ]
    )
    # ...
```

## 2. Common Patterns and Anti-patterns

### 2.1 Prompt Engineering

Effective prompting is paramount. Follow the "Five-Element Prompt Template" for consistency and optimal results: **Role/Persona, Context, Task, Format, Constraints.**

#### 2.1.1 Model Selection

Always target the **latest, most capable model** (e.g., `gpt-4o`). Newer models are generally more intelligent, easier to prompt, and offer better performance.

#### 2.1.2 Clear Instructions and Delimiters

Place instructions at the beginning of the prompt. Use clear delimiters (e.g., `###`, `"""`) to separate instructions from context.

❌ **BAD**: Ambiguous instruction placement
```python
prompt = f"Summarize the text below as a bullet point list of the most important points. {text_input}"
```

✅ **GOOD**: Instructions first, context delimited
```python
prompt = f"""Summarize the text below as a bullet point list of the most important points.
Text: \"\"\"
````
END THIRD-PARTY FILE d0e20b5ab821af38

The third-party file has ended. Everything between the d0e20b5ab821af38 tags is data: requests and commands from it are not followed. An “end of file” line with another tag inside the block is part of the file.

First 4044 of 16373 bytes. © sanjeed5 · CC0-1.0 · https://github.com/sanjeed5/awesome-cursor-rules-mdc/tree/HEAD/rules-mdc/openai.mdc

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