OpenAI Cookbook Pro

Developer Tools: Universal Runtime | Universal Developer

An Advanced Implementation Guide to GPT-4.1: Real-World Applications, Prompting Strategies, and Agent Workflows

Welcome to OpenAI Cookbook Pro — a comprehensive, practical, and fully extensible resource tailored for engineers, developers, and researchers working with the GPT-4.1 API and related OpenAI tools. This repository distills best practices, integrates field-tested strategies, and supports high-performing workflows with enhanced reliability, precision, and developer autonomy.

If you're familiar with the original OpenAI Cookbook, think of this project as an expanded version designed for production-grade deployments, advanced prompt development, tool integration, and agent design.

🔧 What This Cookbook Offers

Structured examples of effective prompting for instruction following, planning, tool usage, and dynamic interactions.
Agent design frameworks built around persistent task completion and context-aware iteration.
Tool integration patterns using OpenAI's native tool-calling API — optimized for accuracy and reliability.
Custom workflows for coding tasks, debugging, testing, and patch management.
Long-context strategies including prompt shaping, content selection, and information compression for up to 1M tokens.
Production-aligned system prompts for customer service, support bots, and autonomous coding agents.

Whether you're building an agent to manage codebases or optimizing a high-context knowledge retrieval system, the examples here aim to be direct, reproducible, and extensible.

Getting Started

OpenAI Cookbook Pro assumes a basic working knowledge of OpenAI’s Python SDK, the GPT-4.1 API, and how to use the functions, tools, and system prompt fields.

If you're new to OpenAI's tools, start here:

This project builds on those foundations, layering in advanced workflows and reproducible examples for:

Task persistence
Iterative debugging
Prompt shaping and behavior targeting
Multi-step tool planning

Prompting for Instruction Following

GPT-4.1’s instruction-following capabilities have been significantly improved. To ensure the model performs consistently:

Be explicit. Literal instruction following means subtle ambiguities may derail output.
Use clear formatting for instruction sets (Markdown, XML, or numbered lists).
Place instructions at both the top and bottom of long prompts if the context window exceeds 100K tokens.

Example: Instruction Template

# Instructions
1. Read the user’s message carefully.
2. Do not generate a response until you've gathered all needed context.
3. Use a tool if more information is required.
4. Only respond when you can complete the request correctly.

See /examples/instruction-following.md for more variations and system prompt styles.

Designing Agent Workflows

GPT-4.1 supports agentic workflows that require multi-step planning, tool usage, and long turn durations. Designing effective agents starts with a disciplined structure:

Include Three System Prompt Anchors:

Persistence: Emphasize that the model should continue until task completion.
Tool usage: Make it clear that it must use tools if it lacks context.
Planning: Encourage the model to write out plans and reflect after each action.

See /agent_design/swe_bench_agent.md for a complete agent example that solves live bugs in open-source repositories.

Tool Use and Integration

Leverage the tools parameter in OpenAI's API to define functional calls. Avoid embedding tool descriptions in prompts — the model performs better when tools are registered explicitly.

Tool Guidelines

Name your tools clearly.
Keep descriptions concise but specific.
Provide optional examples in a dedicated # Examples section.

Tool-based prompting increases reliability, reduces hallucinations, and helps maintain output consistency.

Chain of Thought and Planning

While GPT-4.1 does not inherently perform internal reasoning, it can be prompted to think out loud:

First, identify what documents may be relevant. Then list their titles and relevance. Finally, provide a list of IDs sorted by importance.

Use structured strategies to enforce planning:

Break down the query.
Retrieve and assess context.
Prioritize response steps.
Deliver a refined output.

See /prompting/chain_of_thought.md for templates and performance impact.

Handling Long Contexts

GPT-4.1 supports up to 1 million tokens. To manage this effectively:

Use structure: XML or markdown sections help the model parse relevance.
Repeat critical instructions at the top and bottom of your prompt.
Scope responses by separating external context from user queries.

Example Format

<instructions>
Only answer based on External Context. Do not make assumptions.
</instructions>
<user_query>
How does the billing policy apply to usage overages?
</user_query>
<context>
<doc id="12" title="Billing Policy">
[...]
</doc>
</context>

See /examples/long-context-formatting.md for formatting guidance.

Code Fixing and Diff Management

GPT-4.1 includes support for a tool-compatible diff format that enables:

Patch generation
File updates
Inline modifications with full context

Use the apply_patch tool with the recommended V4A diff format. Always:

Use clear before/after code snippets
Avoid relying on line numbers
Use @@ markers to indicate scope

See /tools/apply_patch_examples/ for real-world patch workflows.

Real-World Deployment Scenarios

Use Cases

Support automation using grounded answers and clear tool policies
Code refactoring bots that operate on large repositories
Document summarization across thousands of pages
High-integrity report generation from structured prompt templates

Each scenario includes:

Prompt formats
Tool definitions
Behavior checks

Explore the /scenarios/ folder for ready-to-run templates.

Prompt Engineering Reference Guide

A distilled reference for designing robust prompts across various tasks.

Sections:

General prompt structures
Common failure patterns
Formatting styles (Markdown, XML, JSON)
Long-context techniques
Instruction conflict resolution

Found in /reference/prompting_guide.md

API Usage Examples

Includes starter scripts and walkthroughs for:

Tool registration
Chat prompt design
Instruction tuning
Streaming outputs

All examples use official OpenAI SDK patterns and can be run locally.

Contributing

We welcome contributions that:

Improve clarity
Extend agent workflows
Add new prompt techniques
Introduce tool examples

To contribute:

Fork the repo
Create a new folder under /examples or /tools
Submit a PR with a brief description of your addition

License

This project is released under the MIT License.

Acknowledgments

This repository builds upon the foundational work of the original OpenAI Cookbook. All strategies are derived from real-world testing, usage analysis, and OpenAI’s 4.1 Prompting Guide (April 2025).

For support or suggestions, feel free to open an issue or connect via OpenAI Developer Forum.