How to Write Prompts for ChatGPT: The Only Structure I Use (and Why It Works)

The biggest lie about prompting is that it's "just asking nicely."

If you've used ChatGPT for more than a week, you've felt the real problem: you write something that seems clear, and the model still drifts. It guesses what you meant. It returns the right shape of answer but the wrong content. Or it gives you a long, polite essay when you needed a decision and a checklist.

Here's what I've noticed: prompt quality isn't about clever wording. It's about giving the model a small "operating environment" where the task is unambiguous, the constraints are testable, and the output is easy to validate.

Researchers keep running into the same thing from a different angle: models can look correct while being internally inconsistent, and "forward-only" success can hide brittle reasoning [3]. That's exactly what you're fighting when a prompt works once and then fails on the next run with slightly different inputs.

So let's talk about how to write prompts for ChatGPT in a way that holds up.

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Treat your prompt like an interface, not a paragraph

When people say "prompt engineering," the useful interpretation is: you're designing an interface contract for a probabilistic program.

A good contract has three parts.

First: define the job. Second: define the inputs. Third: define what counts as a correct output.

You can see this pattern clearly in controlled experiments where prompts enforce strict schemas and verification steps. For example, in a round-trip consistency benchmark, the authors require outputs in a fixed JSON shape and even add repair prompts if the output doesn't match the schema [3]. That's not "prompt magic." That's basic interface discipline.

And in a separate study on LLM-assisted abstracts, the researchers ground the model in explicit guidelines (word count, what must be included, what must be omitted) and then give the model a very specific rewriting instruction [1]. Same move: constraints that can be checked.

The model doesn't need inspiration. It needs a spec.

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My default prompt structure (the one I reuse everywhere)

I rarely start with "Write X about Y." I start with a structure that forces clarity.

In practice, I think of prompts as five blocks: Role, Goal, Context, Constraints, and Output Format.

The point isn't that the model "believes" the role. The point is that role + goal narrows the space of plausible completions.

The abstract study I mentioned basically does this: it sets "assume you are the author," supplies external guidelines, and tells the model exactly how to start the output ("start with 'Abstract:'") [1]. It's mundane, and it works.

The catch is you need to write these blocks as if someone else will maintain them later. Because "future you" is that maintainer.

Here's a template you can paste and reuse:

Role: You are a [specific expert] helping me [specific job].
Goal: Produce [deliverable] that achieves [success criteria].

Context:
- Audience: [...]
- Inputs: [...]
- What I already have: [...]
- What I'm trying to decide/do next: [...]

Constraints:
- Must include: [...]
- Must avoid: [...]
- If information is missing, ask me exactly N questions before answering.
- If you're uncertain, label assumptions explicitly.

Output format:
Return:
1) ...
2) ...
3) ...

Notice what's missing: vibes. Notice what's present: testable requirements.

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Make the model ask questions before it hallucinates

Most "bad prompting" is actually "missing requirements." And missing requirements force the model to guess.

One reason I like the benchmark-style prompting mindset is that it treats failures as format and spec violations, not "the model being weird." In structured evaluation setups, if the output is malformed, the system repairs it back into the required schema [3]. You can copy that idea manually by adding a gate:

"Ask me up to 3 clarifying questions. Don't answer until you have them."

This is especially valuable in product work, where the model will happily invent constraints you never stated. If you don't force clarification, you'll get confident nonsense that looks shippable.

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Use formatting as a control surface (not decoration)

Delimiters and explicit sections aren't aesthetics. They're parsing hints.

When you separate context from instructions, you reduce the chance the model treats your raw materials as things to rewrite or argue with. When you pin output to a schema, you reduce "creative formatting."

Again, this shows up in research workflows: prompts that require answers in a specific JSON object, optionally wrapped in tags, plus post-processing to canonicalize outputs [3]. The point is reliability.

In ChatGPT, you can get 80% of that value by doing two things:

Use clear section headers (Context:, Task:, Output:) and keep them consistent across prompts.

Delimit long inputs so the model knows what's "data" versus "instruction."

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Don't over-trust "step-by-step" as a magic spell

A lot of prompt advice says "just tell it to think step by step." Sometimes that helps. Sometimes it just makes the model produce longer text.

There's also research suggesting instruction-following behavior can be brittle and sensitive to prompting patterns and evaluation settings [2]. Translation: the way you phrase the request can change performance in non-obvious ways, and what works in one setup might fail in another.

My take: don't use "step-by-step" as a ritual. Use it when you actually want intermediate artifacts you can inspect, like assumptions, derived constraints, or a plan you'll approve before the final answer.

If you only want the final output, ask for the final output and add validation requirements instead.

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Practical examples (real prompts you can copy)

I'll keep these grounded and usable. These examples borrow the "Role / Context / Constraints / Output Format" shape you'll see in community prompt-sharing, but with the stricter interface mindset layered on top [4], [5].

Role: You are a senior product analyst.
Goal: Help me decide whether feature X is worth building next sprint.

Context:
- Product: B2B SaaS for invoice reconciliation
- Users: finance ops managers
- Proposed feature: "auto-categorize vendors" using rules + LLM suggestions
- Current pain: manual tagging takes ~2 hours/week per customer
- Constraints: we cannot store raw invoices longer than 30 days

Constraints:
- Ask exactly 3 clarifying questions first.
- Then provide a recommendation with explicit assumptions.
- Avoid generic advice.

Output format:
A) 3 questions
B) Recommendation (Yes/No/Not yet)
C) Rationale (max 200 words)
D) Risks (bulleted, max 6)
E) Next experiment to run (one, concrete)

Here's a content repurposing prompt inspired by a popular community example, but tightened with clearer contract terms [4]:

Role: You are my content repurposing assistant.
Goal: Convert one source text into platform-specific drafts.

Context:
- Brand tone: friendly, clear, slightly opinionated
- Audience: developers + product managers
- Source text: <<<PASTE BELOW>>>

Constraints:
- Keep the core claims the same; do not invent facts.
- If the source lacks a concrete example, ask me for one instead of making it up.
- No emojis. No hashtags unless requested.
- Use short paragraphs. Avoid hype.

Output format:
1) LinkedIn post (140-180 words, 1 hook line + 3 short paragraphs + 1 CTA question)
2) X thread (6 tweets, each ≤ 240 chars, tweet 1 must contain the hook)
3) Email (subject line + 90-120 word body)

And here's a persona prompt, similar to what people share in the wild, but with the one change that matters: you remove fluff like "Strategic Chain-of-Thought" and replace it with observable outputs [5].

Role: You are a customer persona researcher.
Goal: Turn basic demographics into a psychographic persona for messaging.

Context:
Customer basics:
- Age:
- Role:
- Industry:
- Income band:
- Biggest job-to-be-done:
- Current workaround:

Constraints:
- If any of the above fields are missing, ask me for them before generating.
- Keep output under 500 words.
- Avoid therapy jargon and generic filler.

Output format:
Persona card with:
- Name (fictional)
- Core motivations (3)
- Core fears (3)
- Decision triggers (3)
- Objections (3)
- Messaging do/don't (4 lines total)

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The habit that makes prompts "work": treat outputs as test cases

If you want ChatGPT to be consistently useful, stop thinking "Did it answer?" and start thinking "Can I verify this output quickly?"

That's the thread connecting these sources: strict guidelines for abstracts [1], evaluation setups that enforce schemas and repair malformed outputs [3], and empirical evidence that behavior can depend heavily on prompting patterns [2].

Your prompt should make it easy to spot failure.

Try this the next time you prompt: add one sentence that defines what a wrong answer looks like. You'll be surprised how often that alone snaps the model back into line.

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References

Documentation & Research

1. LLM or Human? Perceptions of Trust and Information Quality in Research Summaries - arXiv cs.CL (CHI 2026 submission)
   https://arxiv.org/abs/2601.15556

2. Do Instruction-Tuned Models Always Perform Better Than Base Models? Evidence from Math and Domain-Shifted Benchmarks - arXiv cs.LG
   https://arxiv.org/abs/2601.13244

3. Can LLMs Compress (and Decompress)? Evaluating Code Understanding and Execution via Invertibility - arXiv cs.LG
   https://arxiv.org/abs/2601.13398

Community Examples

4. This tiny ChatGPT prompt replaced my entire weekly content process - r/ChatGPTPromptGenius
   https://www.reddit.com/r/ChatGPTPromptGenius/comments/1qir7cy/this_tiny_chatgpt_prompt_replaced_my_entire/

5. This AI Prompt Turn Basic Customer Information Into Real Insights and Help Create Marketing Strategy that Connects with People - r/PromptEngineering
   https://www.reddit.com/r/PromptEngineering/comments/1qkm57l/this_ai_prompt_turn_basic_customer_information/