Tips for Good Prompt Engineering According to OpenAI (ChatGPT)

1st Official Post
    • Official Post

    September 2026 update:

    Post

    RE: Tipps für gutes Prompt Engineering laut OpenAI (ChatGPT)

    Update September 2026: This thread summarizes OpenAI's recommendations from 2024. Since then, OpenAI has rewritten its documentation for the GPT-6 models, and quite a few things have shifted. Here's the current state, based exclusively on official sources; the links are below.

    For ChatGPT, three steps are enough

    For regular ChatGPT users, OpenAI has published a basic framework in the Academy: First, clearly state the task, ideally using an action verb such as “plan,” “design,” or…
    Daniel Böhmel
    September 27, 2026 at 9:18 PM

    Original:

    Hi everyone,

    Today, I’d like to share some valuable tips with you on prompt engineering and design. When you generate text or work with AI, it can sometimes be challenging to achieve the desired results. This is where precise prompt engineering and good design come into play. With a few simple strategies, you can significantly improve the quality and relevance of the generated content. In the following sections, you’ll learn how to use reference texts, provide clear instructions, and ask targeted questions to achieve better results. Enjoy reading and trying them out!

    Tips for Good Prompt Engineering

    1. Use reference texts:
      • Strategy: Give the model specific texts or paragraphs for guidance.
      • Example:
        • Prompt: "Use the following text to create a summary: [TEXT]"
        • Result: The model provides a precise summary based on the provided text.
    2. Clear instructions:
      • Strategy: Formulate detailed and precise instructions.
      • Example:
        • Prompt: "Create a 200-word summary of the article about [TOPIC], taking the latest research into account."
        • Result: A focused and detailed summary.
    3. Ask targeted questions:
      • Strategy: Formulate specific questions to get targeted answers.
      • Example:
        • Prompt: "What main reasons does the author give for [TOPIC]?"
        • Result: A direct and relevant answer.

    These techniques help improve the accuracy and relevance of generated texts.

    Conclusion:

    With precise prompt engineering and thoughtful design, you can significantly improve the quality of your AI-generated texts. By providing reference texts, giving clear instructions, and asking targeted questions, you can achieve more precise and relevant results. These simple strategies make the difference between mediocre and outstanding output. Give it a try and see for yourself how your texts improve in quality! Good luck applying these tips!

    Source:
    https://platform.openai.com/docs/guides/prompt-engineering/strategy-provide-reference-text

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    Also, ganz ehrlich, ich hab schon öfter mal gedacht, dass diese "klare Anweisungen"-Nummer irgendwie leichter klingt als sie ist... 😅 Ich tippe manchmal so um den heißen Brei rum und wundere mich dann, warum die KI irgendwas ganz anderes ausspuckt. Ist jetzt vielleicht ne blöde Frage, aber wie detailliert muss man denn wirklich werden? Ich hab das Gefühl, wenn ich zu viel schreibe, liest das eh keiner mehr – oder die KI schaltet einfach ab und halluziniert irgendwas dazu? Bin mir da echt nicht sicher.

    Und das mit den Referenztexten – ich vergesse dauernd, dass ich einfach 'nen Beispieltext reinkopieren kann! Wäre ja eigentlich voll einfach... Naja, Memo an mich selbst: lieber einmal mehr copy-pasten als wieder ewig an der Fragestellung basteln. 🤦‍♂️

    I often feel the same way as you do when prompting, MathDeLux—the paradox between being “precise” and not “overwhelming” the model occupies my thoughts too. Sometimes it feels almost like a balancing act: if you provide too little context, you usually get something completely inconsequential; but if you pack in too much information or too many details, you end up with such vague asides that the model itself no longer knows what it is supposed to respond to. I’ve already noticed that language models like GPT-4 tend to “get lost” more quickly with complex tasks than with simpler prompts, which are easier to formulate anyway.

    One observation from my sessions: there’s something like a “sweet spot.” Especially with longer prompts, structure and clarity suddenly become more important than the sheer number of words. I then try to organize things logically, as I would when programming—using sections, bullet points, or even short intermediate questions. Sometimes something as simple as this is enough: “First do x and then explain y; at the end, provide a summary in 1–2 sentences.” Personally, I even prefer that to a single, endlessly long prompt with everything written on one long line. Occasionally, I also specify exactly how detailed something should be (“briefly,” “a maximum of 5 sentences,” “bullet points only”)—and you can tell that models actually follow those instructions quite obediently as long as the structure remains understandable.

    Something else I find interesting: the tone you use also has a massive impact on the result. A bit like with real people… For example, if I write a prompt along the lines of “Imagine you’re a teacher for 10-year-olds…,” I suddenly get answers that are much more vivid and easier to understand. Maybe there’s a little trick in there for guiding a kind of “expectation”—at least it often works better for me than simply providing data.

    What still leaves me puzzled is why nonsense sometimes comes out despite using highly precise prompts. Last month, I had a conversation with GPT‑4o about quantum computers, fully referenced and including citations—yet some things were simply made up. 🤔 Maybe there are limits to prompt engineering that you simply can’t overcome?

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    One thing I'm still missing in this whole prompt discussion: How much does the “how” of the question actually influence the model's creativity? I mean, if I write something like “Imagine you're a news editor who has to get across everything important in 3 concise sentences” instead of “Summarize,” for example—does the model come up with different ideas? I've noticed that with GPT‑4, even small changes to the prompt (such as adding more of a role description or practical context) can change the answers quite a bit… or am I just imagining it? 🤔

    Maybe this is overthinking it a bit, but: Do you sometimes try out completely absurd roles or scenarios to trigger a different language level, for example? I'd be curious to hear what has given you the biggest “aha” moment...
    Off-topic: My tea is almost cold… well, you know how it is.

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    • Official Post

    Update September 2026: This thread summarizes OpenAI's recommendations from 2024. Since then, OpenAI has rewritten its documentation for the GPT-6 models, and quite a few things have shifted. Here's the current state, based exclusively on official sources; the links are below.

    For ChatGPT, three steps are enough

    For regular ChatGPT users, OpenAI has published a basic framework in the Academy: First, clearly state the task, ideally using an action verb such as “plan,” “design,” or “research.” Second, provide helpful context, including files or images. Third, describe the desired result: tone, format, length, audience, and constraints.

    Four tips to go with that: break large tasks into smaller steps, be specific without overloading the prompt, explicitly ask for multiple options when needed, and say what takes priority, such as accuracy or creativity. The second point is often overlooked. More text does not automatically make a prompt better; OpenAI itself says that too much additional information can worsen the response.

    How to structure a good prompt

    For more complex prompts, such as those for custom GPTs, projects, or the API, OpenAI names four components in a fixed order: identity (the assistant's purpose and style), instructions (what it should and should never do), examples (inputs with the desired output), and context (additional knowledge it otherwise does not have). The context belongs at the end.

    For organization, OpenAI recommends Markdown headings and lists, along with XML tags to cleanly separate, for example, a reference document. And when it comes to examples, several different ones are better than three that are nearly identical.

    By the way, assigning a role at the beginning is not a trick from the early days; it remains the first component at OpenAI. What this looks like in practice is explained in our magazine article Assigning Roles in ChatGPT Prompts.

    Reasoning models need less guidance

    This is the biggest difference from 2024. OpenAI compares a reasoning model to an experienced colleague: you give them a goal and leave the details to them. A classic GPT model, on the other hand, is more like someone new to the profession who works best with precise instructions. So if you are working in reasoning mode, you can usually skip step-by-step instructions. Here, less really is more.

    What's new with GPT-6

    Two behaviors are likely to stand out to many users in everyday use. First, GPT-6 asks follow-up questions more often when an answer could substantially change the result, and it sometimes stops where you would actually expect it to simply continue. If you don't want that, explicitly tell the model to make reasonable assumptions and complete the task.

    Second, GPT-6 tends to respond in detail with lists, tables, and Markdown, and it reuses similar phrasing across multiple sessions. So if you want prose, you have to say so. OpenAI even provides a specific template to avoid stock phrases, explicitly naming worn-out words such as “delve” or “leverage,” as well as summarizing concluding sentences.

    For German texts, my own version of this idea works well:

    Quote

    Write in clear paragraphs, with one idea per paragraph. Use lists only when the points are genuinely of equal importance. Avoid stock phrases such as “plays a crucial role” or “in today's world,” and do not include a summary at the end. State the most important point early.

    One limitation

    A large portion of OpenAI's documentation is aimed at developers working through the API. Not every recommendation can be transferred to the ChatGPT chat one-to-one. OpenAI also emphasizes that even different versions of the same model may respond differently to the same prompt. Testing therefore remains essential.

    If you want to dive deeper, our magazine features the ultimate guide to perfect AI prompts with many examples. I'm curious: Have you also noticed GPT-6 asking more follow-up questions, and how do you deal with it?

    Sources:

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    I find the three steps from the update to be a useful basic framework: task, relevant context, desired outcome. When it comes to the level of detail, a clear prioritization helps me more than a lengthy role description: What is the goal, which information is binding, and how can you tell whether an answer is good? Especially with long reference texts, I would also specify which sections should actually be taken into account—otherwise you quickly get a formally polished but substantively diluted answer.

    I see creativity similarly to [Top-Gun] Big_Cool: The wording does indeed change the search space, but “You are a news editor” is not automatically better than concrete criteria such as tone, length, target audience, and permitted inferences. Roles mainly help with style, while examples and a clear output format often contribute more to reliability. Has anyone already systematically compared the new OpenAI recommendations with Claude or Gemini? I’d be interested to know whether the models respond similarly well to the same structured instructions.

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