GPT-5官方提示词（GPT-5 prompting guide）

一、英文原文

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

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.

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.

If you’re willing to be maximally prescriptive, you can even set fixed tool call budgets, like the one below. The budget can naturally vary based on your desired search depth.

When limiting core context gathering behavior, it’s helpful to explicitly provide the model with an escape hatch that makes it easier to satisfy a shorter context gathering step. Usually this comes in the form of a clause that allows the model to proceed under uncertainty, like “even if it might not be fully correct” in the ab