| # [OpenAI Cookbook Pro](https://chatgpt.com/canvas/shared/6825e9f6e8d88191bf9ef4de00b29b0f) | |
| ### Developer Tools: [Universal Runtime](https://github.com/davidkimai/universal-runtime) | [Universal Developer](https://github.com/davidkimai/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. | |
| ## 📘 Table of Contents | |
| 1. [Getting Started](#getting-started) | |
| 2. [Prompting for Instruction Following](#prompting-for-instruction-following) | |
| 3. [Designing Agent Workflows](#designing-agent-workflows) | |
| 4. [Tool Use and Integration](#tool-use-and-integration) | |
| 5. [Chain of Thought and Planning](#chain-of-thought-and-planning) | |
| 6. [Handling Long Contexts](#handling-long-contexts) | |
| 7. [Code Fixing and Diff Management](#code-fixing-and-diff-management) | |
| 8. [Real-World Deployment Scenarios](#real-world-deployment-scenarios) | |
| 9. [Prompt Engineering Reference Guide](#prompt-engineering-reference-guide) | |
| 10. [API Usage Examples](#api-usage-examples) | |
| ## 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: | |
| * [OpenAI Platform Documentation](https://platform.openai.com/docs) | |
| * [Original OpenAI Cookbook](https://github.com/openai/openai-cookbook) | |
| 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 | |
| ```markdown | |
| # 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**: | |
| ```markdown | |
| 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: | |
| 1. Break down the query. | |
| 2. Retrieve and assess context. | |
| 3. Prioritize response steps. | |
| 4. 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 | |
| ```xml | |
| <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: | |
| 1. Fork the repo | |
| 2. Create a new folder under `/examples` or `/tools` | |
| 3. 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](https://github.com/openai/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](https://community.openai.com). | |