OpenAI has released a comprehensive prompting guide for its new GPT-4.1 family of models, highlighting significant improvements in coding capabilities, instruction following, and long context handling compared to GPT-4o. According to the guide published by OpenAI, developers may need to migrate their prompts because GPT-4.1 follows instructions more literally than previous versions, which tended to more liberally infer user intent.
The guide emphasizes that GPT-4.1 is highly steerable and responsive to well-specified prompts, making it particularly suitable for agentic workflows. When implementing agentic capabilities, OpenAI recommends including three key components in system prompts: persistence reminders to ensure the model completes multi-message tasks, tool-calling instructions to prevent hallucinations, and optional planning guidance for explicit reasoning.
For tool usage, the company advises developers to use the official tools field in API requests rather than manually injecting tool descriptions into prompts. Testing showed this approach improved performance by 2% on benchmark tasks. Clear naming and detailed descriptions for tools and parameters are also recommended to ensure appropriate usage.
GPT-4.1’s long context capabilities extend to 1 million tokens, making it effective for document parsing, re-ranking, and multi-hop reasoning tasks. However, OpenAI notes that performance may degrade when complex reasoning across the entire context is required. For optimal results with long contexts, instructions should be placed at both the beginning and end of provided materials.
Although GPT-4.1 is not classified as a reasoning model, prompting it to “think step by step” (chain of thought) can improve output quality by breaking problems into manageable pieces. The trade-off is higher cost and latency due to increased token usage. The guide provides specific prompting templates for implementing chain-of-thought reasoning, including strategies for analyzing queries and relevant context.
The guide addresses instruction following, noting GPT-4.1’s exceptional performance in this area. Developers can precisely control outputs by providing explicit specifications about desired behavior. When existing prompts don’t work as expected, OpenAI recommends checking for conflicting instructions, adding examples of desired behavior, and ensuring instructions are clear and specific.
For structured prompts, OpenAI suggests starting with sections for role and objective, instructions (with sub-categories as needed), reasoning steps, output format, examples, context, and final instructions. The company also provides guidance on selecting effective delimiters, recommending Markdown as a starting point, with XML and JSON as alternatives depending on the use case.
The guide concludes with an appendix on generating and applying file diffs, highlighting GPT-4.1’s substantially improved capabilities in this area compared to previous models. Several recommended diff formats are provided, with emphasis on approaches that avoid line numbers and clearly delineate replaced and replacement code.