OpenAI recommends less rigid instructions for AI

OpenAI recommends that teams using AI review overly detailed or rigid instructions in its guide to the GPT-6 model family, published on October 2, 2026. Rather than prescribing every step, the document proposes clarifying the desired outcome, context, limits and conditions under which the work is considered complete.

For businesses that have accumulated many prompt templates and rules for AI assistants, the issue is not just choosing a new model. Instructions that were once useful may need to be adjusted as task-handling capabilities change. However, the document provides no evidence that all long prompts are less effective or that shortening instructions always improves results.

From fixed procedures to clear task assignments

In OpenAI’s guide to building with the GPT-6 model family, the section on prompts and skills emphasizes that the models now handle nuance and ambiguity better. OpenAI argues that overly specific instructions can sometimes hinder results in situations where they previously helped.

The proposed approach begins with a clear task: what needs to be produced, who it is for, what information and constraints apply, and what counts as complete. This is a change in how work is assigned, not advice to abandon all rules.

For example, when asking AI to compile a report, rigidly specifying the number of paragraphs and the order of every sentence may not accurately reflect the input documents. An outcome-focused request would identify the intended audience, the issues that must be clarified and how to handle missing information. Genuinely necessary constraints, such as a data format that other software can accept, must still be retained.

For office users, this principle builds on how to assign tasks and check results when using AI to support work: describing the goal does not replace providing data or verifying the output.

Skills and AGENTS.md also need updating

Documents and tools selected according to the context of the AI task

OpenAI does not limit its recommendations to the prompt input field. The document also discusses skills and AGENTS.md, components that provide reusable instructions for AI agents, particularly in software development.

Skills need to state clearly when they should be used

For skills, OpenAI proposes concise descriptions that clearly state when they apply, with supporting documents added only when needed. The document also recommends replacing rigid procedures with guidance suited to the model the team is using.

This does not mean removing mandatory procedures. Security controls, legal requirements and output formats remain constraints. What can be more flexible is how AI organizes its work within the permitted scope.

AGENTS.md needs to connect instructions to context

For AGENTS.md, OpenAI recommends explaining when a document or test is relevant. The document also proposes explicitly allowing safe routine operations, such as running local tests with disposable data and no access to the production environment.

This is the difference between a list of commands and operating instructions with context. An agent needs to know which situations a rule applies to, rather than having to treat every requirement as mandatory for every task.

“Complete” must include checking the results

The process of carrying out, checking and handing over AI work

Another topic in the guide is defining how far to pursue a task. OpenAI proposes clearly describing what completion entails: making changes, running tests, observing the results and fixing any errors found.

In software development, producing code does not mean the work is complete. For reports, including all the sections does not guarantee that the figures are correct or the reasoning is well-founded. Completion criteria help distinguish a product that appears complete from one that has undergone the necessary checks.

OpenAI also proposes requesting a brief handoff that states what changed, what was checked and what still needs attention. This format helps recipients assess the scope of the work without having to infer it from a generic confirmation.

Even so, an AI’s claim that it has checked something does not itself prove that the result is correct. In workflows involving tools, the handoff information needs to be cross-checked against test results or corresponding evidence.

Greater flexibility does not mean unlimited autonomy

OpenAI’s guide pairs prompt adjustments with the need to define decision boundaries: what AI may choose on its own and when it must ask the user. The example in the document allows it to organize a summary independently but requires confirmation before changing the project’s scope.

This is also why reducing rigid instructions must be distinguished from expanding authority to act. An agent can be flexible in how it carries out work while still being limited in the data it can access and the changes it is allowed to make. The article on when AI agents need to ask permission examines this boundary separately.

For small businesses, the change also involves employees’ task-assignment skills. AI training for small businesses therefore matters beyond teaching people how to use an interface: users need to know how to express goals and recognize results that are not sufficiently reliable.

There is not yet a basis for a universal prompt formula

A handoff linking work status to source documents and next steps

The source provided is guidance from the model provider, not an independent comparative study of the effectiveness of different prompt styles. The document does not publish quantitative improvements from reducing rigidity, nor does it identify an optimal prompt length.

This recommendation is therefore better suited to reviewing instructions that no longer serve their purpose than to deleting prompts that work well en masse. When trying a new version of the instructions, results still need to be evaluated on representative tasks. OpenAI’s own document also emphasizes testing before deployment, using metrics such as task completion, latency and cost per successful task.

Frequently asked questions

Should you discard existing prompt templates when switching AI models?

Do not discard them en masse. Keep the current version as a baseline, test the revised version on the same set of tasks, and review any errors, output quality and processing time before replacing it.

Is a prompt requiring JSON output considered too rigid?

Not necessarily. If another system processes the output, requiring JSON and a specific field structure is a necessary functional constraint. Output constraints need to be distinguished from prescribing unnecessary steps.