Gpt5 Prompting Guide

# GPT-5 prompting guide

GPT-5, our newest flagship model, represents a substantial leap forward in agentic task performance, coding, raw intelligence, and steerability.

While we trust it will perform excellently “out of the box” across a wide range of domains, in this guide we’ll cover prompting tips to maximize the quality of model outputs, derived from our experience training and applying the model to real-world tasks. We discuss concepts like improving agentic task performance, ensuring instruction adherence, making use of newly API features, and optimizing coding for frontend and software engineering tasks - with key insights into AI code editor Cursor’s prompt tuning work with GPT-5.

We’ve seen significant gains from applying these best practices and adopting our canonical tools whenever possible, and we hope that this guide, along with the [prompt optimizer tool](https://platform.openai.com/chat/edit?optimize=true) we’ve built, will serve as a launchpad for your use of GPT-5. But, as always, remember that prompting is not a one-size-fits-all exercise - we encourage you to run experiments and iterate on the foundation offered here to find the best solution for your problem.

## Agentic workflow predictability

We trained GPT-5 with developers in mind: we’ve focused on improving tool calling, instruction following, and long-context understanding to serve as the best foundation model for agentic applications. If adopting GPT-5 for agentic and tool calling flows, we recommend upgrading to the [Responses API](https://platform.openai.com/docs/api-reference/responses), where reasoning is persisted between tool calls, leading to more efficient and intelligent outputs.

### Controlling agentic eagerness

Agentic scaffolds can span a wide spectrum of control—some systems delegate the vast majority of decision-making to the underlying model, while others keep the model on a tight leash with heavy programmatic logical branching. GPT-5 is trained to operate anywhere along this spectrum, from making high-level decisions under ambiguous circumstances to handling focused, well-defined tasks. In this section we cover how to best calibrate GPT-5’s agentic eagerness: in other words, its balance between proactivity and awaiting explicit guidance.

#### Prompting for less eagerness

GPT-5 is, by default, thorough and comprehensive when trying to gather context in an agentic environment to ensure it will produce a correct answer. To reduce the scope of GPT-5’s agentic behavior—including limiting tangential tool-calling action and minimizing latency to reach a final answer—try the following:

- Switch to a lower `reasoning_effort`. This reduces exploration depth but improves efficiency and latency. Many workflows can be accomplished with consistent results at medium or even low `reasoning_effort`.
- Define clear criteria in your prompt for how you want the model to explore the problem space. This reduces the model’s need to explore and reason about too many ideas: