OpenAI's new prompting guide: stop overthinking, start with the result

Key Takeaways

• OpenAI now recommends leading with your desired result, not detailed step-by-step instructions
• Prompts have four optional building blocks: goal, context, output format, and boundaries
• ChatGPT Work handles multi-hour tasks across apps while regular Chat handles quick queries

OpenAI has published a new prompting guide that consolidates its advice into one document. The message to users: stop engineering elaborate prompts and just say what you want. The guide covers both ChatGPT and the new ChatGPT Work product in a single framework, reflecting how the two are merging into one offering.

The timing is notable. This arrives shortly after OpenAI launched ChatGPT Work, a standalone product built on Codex technology and the GPT-5.6 model. That product can spend hours on complex projects, operate across apps and files, and produce finished Excel or Word documents. The guide seems designed to help users actually use these capabilities without getting lost in prompt optimization.

What are the four building blocks of a prompt?

OpenAI structures prompts around four optional components: goal, context, output format, and boundaries. None are required. A short prompt often works fine, and the company says filling in all four only makes sense for bigger tasks.

The core shift here is philosophical. OpenAI 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 states.

This contradicts years of prompt engineering folklore. The community developed elaborate techniques: role-playing ("You are an expert..."), chain-of-thought instructions, carefully structured multi-step queries. OpenAI is now saying most of that is unnecessary overhead. A target audience or format shapes the output more than detailed instructions ever did.

Why constraints work better than scripts

Rather than scripting every move, OpenAI recommends setting one or two hard rules to block unwanted behavior. The examples they give are practical: "Keep the approved dates and budget figures unchanged" or "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. Plugins for Google Drive, Gmail, Slack, and GitHub are available for teams that need them.

For high-stakes work, OpenAI suggests asking ChatGPT to verify its own output. An example: checking whether every action item has an owner and a deadline. This is a lightweight guardrail that costs nothing but adds a verification step.

Chat vs. Work: where does the line fall?

The guide draws a clear distinction. Chat handles quick questions and rewording. Work handles 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.

This is a sensible division. If you're building workflows with tools like Zapier or Make, the implication is clear: get the prompt right interactively before you automate it.

Codex commands for developers

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. "/goal" sets a higher-level objective Codex follows across multiple steps.

For code reviews, users can run "/review" locally or mention "@codex review" in a GitHub comment. You can add an optional focus like "review for security vulnerabilities." This integrates Codex directly into existing developer workflows.

How does this differ from OpenAI's developer docs?

The tone here is deliberately different from OpenAI's recent developer documentation for GPT-5 and GPT-5.5. Those docs 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.

This bifurcation makes sense. Developers building applications need fine-grained control. End users and product teams just need the thing to work. The guide acknowledges that most users were overcomplicating their prompts, and the models have become smart enough that this extra effort is counterproductive.

Frequently Asked Questions

Do I need to use all four building blocks in every prompt?

No. OpenAI says a short prompt often works. Fill in goal, context, format, and boundaries only when the task is complex enough to need them.

What is the difference between ChatGPT Chat and ChatGPT Work?

Chat handles quick questions and rewording. Work handles multi-step tasks that pull in multiple sources, make changes, or produce larger deliverables like reports.

How do I use Codex for code reviews?

Run /review locally or mention @codex review in a GitHub comment. You can add a focus area like "review for security vulnerabilities."

Should I still use chain-of-thought prompting?

OpenAI now recommends leading with the result rather than describing step-by-step processes, unless the process itself is what you need.

Where do I set preferences that apply to all my ChatGPT sessions?

Go to Settings > Personalization and add them as Custom Instructions. Task-specific preferences stay in individual prompts.

Source: The Decoder / Jonathan Kemper