A Practical Guide to GPT-5.1 Prompting by OpenAI

OpenAI's GPT-5.1 AI model is here, and it is a smarter, more conversational ChatGPT. On paper, GPT-5.1 is a smarter model than GPT-5 while also being more conversational; however, you can only take advantage of its intelligence if you know how to effectively prompt it. To get the most out of an AI model, you need to write effective prompts that it can understand and follow without losing any added information. The GPT-5.1 model itself is designed to balance intelligence and speed for agentic and coding tasks, and introduces a new non-reasoning mode for low-latency interactions similar to GPT-4.1 and GPT-4o.

When you have such a powerful AI model, simple one-line commands can get you results, but to use its full power, you need to prompt effectively. Effective prompting is an art, but it's one built on a few clear, powerful principles. When you combine these strategies into your requests, you can seriously improve the quality, reliability, and consistency of the AI's output.

OpenAI has released a GPT-5.1 Prompting Guide, and in this article, we will go through the main ideas, why they matter, and how to translate them into practical patterns you can apply today. The concepts discussed in this guide will be helpful whether you're a developer, product manager, or just someone trying to get more consistent results from ChatGPT or the API.

A practical GPT-5.1 prompting guide (based on OpenAI’s tips):

1. Migrating to GPT-5.1 without breaking your stack

The OpenAI cookbook starts with migration, because how you move to GPT-5.1 decides what you see later.

• If you're on GPT-4.1, GPT-5.1 with reasoning_effort set to "none" is intended to be the natural upgrade path for many low-latency, non-reasoning workloads. You keep the "classic" feel but gain better calibration and stronger instruction following.
• If you're on GPT-5: OpenAI calls out four main adjustments:
  1. Persistence: GPT-5.1 is more careful with tokens and can be too concise. You should explicitly ask it to continue until a task is fully handled, rather than stopping at analysis or partial solutions.
  2. Output formatting & verbosity: Even with smarter defaults, the model sometimes overshoots on length, so setting clear rules about bullets, headings, and snippet limits can help.
  3. Coding agents: Migrate to the new named apply_patch tool, which significantly reduces patch failures.
  4. Instruction following: Many "weird behaviors" can be fixed by removing conflicting instructions and making your expectations explicit.

Think of migration as an opportunity to clean up your system prompts and make them more opinionated, rather than just swapping a model name.

2. Design the agent's personality

GPT-5.1 is described as "highly steerable," which means you can set what it does and how it behaves. Don't just tell the AI what to do; tell it who to be. The guide shows how you can provide detailed personality traits.

• For example, you can instruct a customer support agent to be "economy-minded with language," avoiding pleasantries when a user is in a hurry but offering a "single, succinct acknowledgment" when the user is warm.

This level of control ensures the AI's voice aligns perfectly with your brand or goal.

In addition to the persona section, the guide suggests adding a "final answer formatting" block, which serves as a mini-rulebook that instructs the model on the required length of the answer based on the request size.

• For a tiny change (very short answer) like fixing a typo, reply in 2–5 sentences, with little or no code.
• For medium change (brief structured answer) like updating one function, reply with a short explanation plus a few bullet points, and at most one or two small code snippets or examples.
• For large changes (summary), like refactoring a file, summarize what changed file by file (or section by section), avoiding giant code dumps.

3. Use user updates to keep humans in the loop

In long, agentic runs like refactoring a codebase or orchestrating tools, the user can't see what's happening. The guide introduces "user updates" (preambles) as a pattern to fix that. You can specify:

Frequency & length

• Provide short updates every few tool calls when something meaningful changes, such as at least one update every fixed number of steps.

Content

• A quick plan before the first tool call (goal, constraints, next steps).
• Concrete outcomes in each update ("found X," "confirmed Y") rather than vague "still working" messages.
• A final recap with a checklist of what was done, what was closed, and why.

You're essentially turning the agent into a collaborator who narrates just enough to keep you comfortable, without dumping logs of information.

4. Get complete, end-to-end solutions

One issue OpenAI noticed was that on long tasks, GPT-5.1 can stop too early. The good news is that this behavior is promptable.

• GPT-5.1 can be instructed to be "extremely biased for action," meaning if the user asks "Should we do X?" and the answer is "yes," the agent should also attempt X, not just recommend it.
• You can instruct it to "persist until the task is fully handled end-to-end," making sensible assumptions to move forward rather than constantly requesting clarification.

These patterns transform the AI from a passive tool into a proactive problem-solver, making it a valuable addition to your system prompt.

5. Give Your AI "Tools" and a Rulebook: 

The model can use external functions, or "tools," to perform actions like searching a database, creating a reservation, or even writing and modifying code.

• The key is to provide a clear name and description of what each tool does, along with a rulebook outlining how and when to use it.
• For example, when creating a restaurant reservation tool, you can explicitly tell the AI to ask, "The full name of the guest, number of guests, and reservation date and time."

For performance, GPT-5.1 can also parallelize tool calls, which are especially useful when scanning a codebase or hitting a vector store, if you encourage it to batch reads and writes.

On top of this, GPT-5.1 introduces a "none" reasoning mode that disables reasoning tokens entirely. That makes it behave more like prior non-reasoning models (GPT-4.1, GPT-4o), while still letting you use hosted tools like web search and file search, and improving custom function-calling performance.

Even in "none," you can tell the model to "plan before each function call and verify outputs" so it thinks carefully about which tools to use and double-checks constraints (like price, spec, brand) before acting.

6. Plan first, then execute

For more complex work with many steps, the guide recommends that you instruct the AI to create and maintain a plan using a dedicated "planning tool." A planning tool is essentially a lightweight to-do list that the agent maintains as they work.

• For Example: You can set rules like "create 2–5 milestone/outcome items" and "mark items complete when done."

This forces the AI to think strategically, track its progress, and ensure it completes a complex project to its conclusion. This is about operational discipline, something engineering teams already care about.

7. Take advantage of apply_patch and shell

GPT-5.1 introduces new, specialized tools that give it the ability to directly interact with digital environments, particularly for coding.

• The apply_patch tool allows the model to suggest and execute changes to a codebase directly, using structured diffs to create, update, or delete files. This is like having a programmer with the ability to write code and seamlessly integrate it into an existing project.
• Furthermore, the new shell tool gives the model a controlled command-line interface. It can propose commands to run on a local system, execute them, and analyze the output. This creates a powerful loop where the AI can autonomously inspect a system, run tests, and gather the necessary data to solve a problem. For creative tasks, you can even enforce a design system, providing rules that ensure any frontend code it generates matches your brand's specific color palette and style guidelines.

8. The Ultimate Trick: Metaprompt like a product manager

Perhaps the most revolutionary technique in the guide is the concept of "metaprompting." Metaprompting allows you to build prompts that define the kind of agent you're creating, not just the immediate task. This is the process of using the AI to debug and refine its own instructions.

For example, when an AI agent isn't behaving as expected, perhaps it's too verbose, uses the wrong tools, or gets stuck in a loop, it can be hard to pinpoint the exact line in a long system prompt that's causing the issue. Metaprompting solves this with a simple, two-step process:

1. Diagnose the Problem: You feed the GPT-5.1 the system prompt along with a few examples of where it failed. You then ask it to act as a prompt engineer and perform a root-cause analysis, identifying the specific lines or contradictory instructions that are likely causing the bad behavior.
2. Propose a Solution: Once you have this analysis, you run a second command. You ask GPT-5.1 to propose "surgical" revisions to the prompt to fix the issues it has just identified, clarifying conflicting rules and tightening vague guidance.

This iterative process of diagnosis and revision allows you to fine-tune your GPT-5.1's performance with incredible precision, effectively teaching it how to improve its own instruction manual.

In Conclusion:

It is fair to say GPT-5.1 will reward people who take prompts seriously, and our guide will help you do so. The GPT-5.1 Prompting Guide makes one thing very clear: the model is strong, but the real leverage comes from how deliberately you interact with it.

• If you migrate thoughtfully.
• Design a clear persona and communication rhythm.
• Keep humans in the loop with structured updates.
• Insist on full end-to-end solutions.
• Lean into tools, planning, and metaprompting.

You get agents that feel less like demos and more like dependable coworkers. For builders, PMs, and teams betting on AI in production, GPT-5.1 gives you more power along with better control. The cookbook gives you the patterns; the next step is adapting them to your own idea of what a good AI agent looks like.