OpenAI 新提示指南：停止过度思考，从结果出发

OpenAI has consolidated its prompting advice into a single guide written for everyday users, not developers. The focus is on four building blocks, practical guardrails, and Codex workflows rather than API parameters or model tuning. The guide arrives shortly after OpenAI launched ChatGPT Work , a standalone product built on Codex technology and the new GPT-5.6 model that can spend hours on complex projects, operate across apps and files, and produce finished Excel or Word documents. It covers both the regular ChatGPT interface and Codex in a single framework, reflecting how the two are converging into one product. The tone is a bit different from OpenAI's recent developer docs for GPT-5 and GPT-5.5 , which focused on API parameters, reasoning-effort levels, and elaborate prompt schemas. The end-user guide drops all of that but keeps the same core idea: start small, say what you want, and only add rules where you need them. OpenAI structures prompts around a goal, context, output format, and boundaries . None are required. A short prompt often works, and filling in all four only makes sense for bigger tasks, the company says. The guide recommends leading with the result, not a sequence of steps. "Describe a process when the process itself matters. Otherwise, leave ChatGPT room to search, compare information, and adjust its approach," the document reads. A target audience or format shapes the output far more than detailed instructions. Rather than scripting every move, OpenAI recommends one or two hard rules to block unwanted behavior. Examples: "Keep the approved dates and budget figures unchanged" and "Prepare the message as a draft. Don't send it." The same less-is-more logic applies to context. Only attach sources that will actually change the answer. The guide lists spreadsheets, PDFs, images, web search, and shared project files as options, along with plugins for Google Drive, Gmail, Slack, and GitHub. For high-stakes work, OpenAI suggests asking ChatGPT to verify its own output, for instance, checking whether every action item has an owner and a deadline. The guide draws a line between Chat for quick questions and rewording, and "Work" for tasks that pull in multiple sources, make changes, or produce larger deliverables like reports. Work tasks burn more credits but pay off when they save time or support important decisions. For recurring tasks, OpenAI suggests refining the prompt manually first, then automating it. Users don't need to nail the first prompt. Follow-ups are the expected way to refine output. Preferences that carry across sessions belong in "Settings > Personalization" as "Custom Instructions." Anything task-specific stays in the prompt. For the coding assistant Codex , OpenAI introduces two ways to influence tasks mid-run. "Steer" adds a message to the current run and redirects it. "Queue" lines up a message for the next one. In the CLI, Enter and Tab serve as shortcuts. Codex runs commands inside a sandbox that restricts file and network access. If a task needs to go beyond those limits, Codex asks for approval. Two slash commands help with multi-step projects: "/plan" tells Codex to analyze the code and propose an approach before making changes, while "/goal" sets a higher-level objective Codex follows across multiple steps. For reviews, users can run "/review" locally or mention "@codex review" in a GitHub comment, with an optional focus like "review for security vulnerabilities." Subscribe to THE DECODER for ad-free reading, a weekly AI newsletter, our exclusive "AI Radar" frontier report six times a year, full archive access, and access to our comment section.