ADAM PULSE Why do prompt-hack lists work, when the words themselves are not magic?
Short answer
There are no magic words. "Explain this to me" is not a bad prompt, "summarise this" is not a bad prompt, and no phrase makes a model suddenly more capable.
What actually goes wrong is underspecification. The model does not know who the answer is for, what you already know, what must be included, what must be left out, how long it should be, or what a good answer would even look like. Prompt-swap lists appear to work because their replacements happen to smuggle some of that information in — not because the words are special.
Anthropic publishes a test that makes this obvious and replaces every list you have seen:
Show your prompt to a colleague with minimal context on the task and ask them to follow it. If they'd be confused, Claude will be too.
That is the whole discipline. If a competent person who has not been in your head could not do the task from your instructions, the model cannot either — and no amount of world-class expert framing changes that.
Why "explain this to me" is not a bad prompt
Because sometimes it is complete. If you ask a model to explain photosynthesis and the answer satisfies you, the prompt worked. There is nothing to fix.
The advice genre that insists every request must become a 300-word template gets this exactly backwards. Prompting is not about making requests longer. It is about adding information when the missing information is what is hurting you, and leaving it out when it is not.
Anthropic's framing is the useful one:
Think of Claude as a brilliant but new employee who lacks context on your norms and workflows. The more precisely you explain what you want, the better the result.
You would not hand a capable new colleague a one-word instruction and then conclude they were unintelligent when the result missed. You would also not send them a five-page brief to reformat a paragraph.
The five things that actually change the answer
Not every prompt needs all five. But when a response comes back generic, one of these is almost always the thing that is missing.
| The question it answers | Symptom when it is missing | |
|---|---|---|
| Task | What am I asking for, precisely? | Output is plausible but not what you wanted |
| Context | What can the model not guess? | Confidently wrong assumptions |
| Audience | Who reads this? | Wrong vocabulary, wrong depth |
| Constraints | What rules must the answer obey? | Too long, wrong format, drops what mattered |
| Success | What would make this a good answer? | Technically responsive, practically useless |
Task: vague verbs produce vague results
"Improve this" is not a task. Rewrite this email so the customer understands the requested action immediately; keep the friendly tone, cut repetition, stay under 150 words is a task. Nothing in the second version is a trick. It just says what "improve" meant.
OpenAI's guidance is blunt about it:
Be specific, descriptive and as detailed as possible about the desired context, outcome, length, format, style, etc
Context: what the model cannot know
"Write a follow-up email" forces the model to guess the recipient, the history, the tone and the goal. It will guess, competently, and the guesses will be wrong in ways that take longer to fix than writing the context would have.
Anthropic names the three pieces worth supplying: what the results are used for, who the audience is, and what success looks like. Most disappointing outputs are missing at least two.
Audience: the cheapest single improvement
Explain zero trust and explain zero trust to a small-business owner who understands basic IT but is not a security professional produce genuinely different answers. Change it again to explain zero trust to a CISO who already knows IAM and segmentation; focus only on implementation trade-offs and you get a third.
Same topic. The audience tells the model what to assume, which vocabulary to use, how deep to go and which examples land.
Constraints: usually worth more than the clever phrasing
"Summarise this report" may be fine. When it is not, constraints fix it faster than rewording:
Summarise this in five bullets. Preserve every date, dollar amount and named company. Remove background an executive can infer. Under 200 words.
OpenAI's guidance specifically warns against the fluffy version — rather than "fairly short, a few sentences only," it recommends something measurable like "Use a 3 to 5 sentence paragraph to describe this product."
Success: the part the lists always miss
"Make this more persuasive" gives the model no target. Compare:
Rewrite this proposal introduction to address the customer's actual objection — they believe changing carriers will cause downtime. By the end of the first paragraph the reader should understand the migration can be staged without interrupting existing service.
Now "persuasive" means something specific, and the model is solving your problem rather than performing persuasiveness.
Does "act as a..." actually help?
Sometimes, and the distinction is worth getting right because this is the one prompt-hack that has a real mechanism behind it.
Anthropic's guidance is that setting a role focuses behaviour and tone, and that even a single sentence makes a difference. But notice what the useful roles have in common:
| Adds nothing but adjectives | Adds usable information |
|---|---|
| Act as a world-class marketing genius | Review this as a B2B marketing director selling managed IT to CFOs of 50–500 person companies |
| Act as an elite consultant | Act as an auditor checking this against CMMC Level 2 evidence requirements |
| You are the best writer alive | You are an editor whose job is cutting 20% without losing meaning |
A good role supplies perspective the model can reason from. A theatrical role supplies flattery. The test is simple: does the role tell the model anything it did not already know about the task? If the phrase would be equally at home in front of any request, it is decoration.
Show, do not describe
"Make it sound natural" is a description. A sample of the voice you want is evidence.
Anthropic is direct about this:
Examples are one of the most reliable ways to steer Claude's output format, tone, and structure.
It recommends three to five, chosen to mirror the real use case, varied enough that the model does not latch onto an accidental pattern, and clearly delimited from the rest of the prompt. OpenAI similarly recommends showing the desired structure rather than describing it, particularly when the output needs to be parsed reliably.
If you want something written in your voice, one paragraph of your actual writing beats any number of adjectives.
Is a longer prompt a better prompt?
No. A longer prompt is worse when the extra length adds contradictory instructions, irrelevant background, an unnecessary persona, repeated requirements, or three tasks that should have been three prompts.
Good prompts are information-efficient, not long. Thirty words that remove the important ambiguity beat three hundred that restate the obvious. And when the first attempt misses, the fix is usually one added sentence rather than a rewrite — OpenAI's own guidance describes prompting as iterative: start, read the answer, adjust the specific thing that was wrong.
Weak to strong, without any magic words
| Weak | Stronger | What was actually added |
|---|---|---|
| Explain this to me | Explain this to a business owner who understands basic networking but has never configured a firewall. One analogy, then three things to remember. | Audience, scope, format |
| Summarise this | Five bullets for an executive. Keep numbers, names, dates and decisions. Cut background that does not change the recommendation. | Audience, format, what must survive |
| Make it shorter | Cut to three sentences without losing the recommendation, the deadline or the reason. Tell me what you removed. | Constraint, protected content, accountability |
| Give me examples | Three concrete examples from healthcare, professional services and manufacturing. Label any example that is hypothetical rather than documented. | Range, honesty requirement |
| Make this more professional | Rewrite so a client gets the main point in five seconds. Plain language, no filler, keep the warmth. | Definition of the desired effect |
| Give me strategies for reducing outages | Five strategies for a multi-site business. For each: cost, difficulty, likely benefit, and the most common way it fails. | Environment, evaluation criteria |
Not one of those improvements is a phrase you needed to memorise. Every one is a piece of information the reader could not have guessed.
What better prompting does not fix
Worth stating plainly, because the prompt-hack genre implies otherwise.
A well-specified prompt gets you a better-shaped answer. It does not make the answer true. A model asked confidently for five sources will produce five confident-looking sources, and the specificity that improved the format did nothing for the facts.
Two habits are worth more here than any prompt:
- Ask for the uncertainty. Label anything you are not confident about, and say what would need checking. Models comply with this readily and it costs one sentence.
- Verify anything that carries consequence. Not by asking the model whether it was right — it will usually say yes — but against the source.
The same applies to anything an AI tool does on your behalf rather than for you. Once a tool can act, a well-worded instruction is not a control — which is the subject of a separate piece.
The reusable structure
When something comes back generic, walk the list:
- Task — what do I want done, in a verb that means something?
- Context — what does it need to know that it cannot infer?
- Audience — who reads this, and what do they already understand?
- Constraints — length, format, tone, what must be preserved, what must go?
- Success — what would make this good rather than merely responsive?
Then apply Anthropic's test. Hand it to a colleague with no context. If they would ask you a question before starting, that question is what your prompt is missing.
Frequently asked questions
Are there magic words that make ChatGPT or Claude give better answers?
No. Phrases like "act as a world-class expert" carry no special power. Prompt-swap lists appear to work because the replacement phrasing happens to add information the original was missing — an audience, a format, a constraint. The information is what helps, not the wording.
Is "explain this to me" a bad prompt?
Not if it gets you what you needed. Prompting is about adding information when the missing information is what is hurting the answer, not about making every request longer. A short prompt that leaves no important ambiguity is a good prompt.
What actually makes a good AI prompt?
Some combination of five things: the task stated in a verb that means something, context the model cannot guess, the audience who will read the answer, constraints on length, format and what must be preserved, and a description of what a good answer would accomplish. Most disappointing answers are missing at least two.
How do I know if my prompt is clear enough?
Anthropic publishes a useful test: show the prompt to a colleague with minimal context on the task and ask them to follow it. If they would be confused, the model will be too. Any question they would ask before starting is what your prompt is missing.
Does telling the AI to "act as" someone improve the answer?
Sometimes. Anthropic notes that setting a role focuses behaviour and tone. But the roles that help supply perspective the model can reason from — "a B2B marketing director selling to CFOs of 50 to 500 person companies" — while roles that only supply adjectives, like "world-class genius," add little. If the phrase would sit equally well in front of any request, it is decoration.
Should prompts be long?
No. Prompts should be information-efficient. A longer prompt is worse if it adds contradictory instructions, irrelevant background, unnecessary personas, repeated requirements or several tasks that should have been separate prompts. Thirty words that remove the real ambiguity beat three hundred that restate the obvious.
How specific should I be about output format?
Specific enough to be measurable. OpenAI's guidance recommends being detailed about context, outcome, length, format and style, and replacing vague phrasing such as "fairly short, a few sentences only" with something concrete like "use a 3 to 5 sentence paragraph."
Do examples in a prompt help?
Considerably. Anthropic describes examples as one of the most reliable ways to steer output format, tone and structure, and recommends three to five that mirror the real use case, vary enough to avoid teaching an accidental pattern, and are clearly separated from the instructions. If you want something in your own voice, a paragraph of your actual writing beats any description of it.
Why does telling the AI who the audience is help so much?
Because it settles several questions at once: how much background knowledge to assume, which vocabulary is appropriate, how much detail is useful and which examples will land. Explaining a concept to a small-business owner and to a security professional are different tasks, and the model cannot tell which one you meant.
What is underspecification in prompting?
Asking for something without supplying the information needed to judge what a good answer would be. The model fills the gaps by guessing, competently and invisibly, and the guesses are usually the reason the output feels generic.
Does a better prompt make the answer more accurate?
Not reliably. Better specification produces a better-shaped answer, not a truer one. A model asked confidently for five sources will produce five confident-looking sources. Ask it to label what it is unsure about, and verify anything consequential against the source rather than by asking the model whether it was right.
What should I do when an AI answer comes back generic?
Work through the five elements rather than rewording. Which is missing — the real task, the context it could not guess, the audience, the constraints, or a definition of success? Adding the missing one is usually a single sentence and works better than a rewrite.
Related articles
- What separates AI agents that ship to production from the ones that stay demos? — what changes once the AI can act rather than answer, and why a well-worded instruction stops being a control.
- What does AI data governance actually require in 2026? — the question underneath every prompt: where does what you typed actually go?
- Shadow AI Risk Assessment — a free, private self-assessment of how AI is already being used in your business.
References
Sourced entirely to the prompting documentation published by the two model vendors themselves — Anthropic's and OpenAI's. That is deliberate. Prompting is the subject with the widest gap between what circulates socially and what the people who build the models actually write down, and almost every claim in the popular genre disappears the moment you check it against the primary guidance.
- Anthropic — Claude prompting best practices— source for the colleague test quoted at the top of this article, for the "brilliant but new employee who lacks context on your norms and workflows" framing, for the three pieces of context worth supplying (what the results are used for, who the audience is, what success looks like), for examples being one of the most reliable ways to steer output format, tone and structure with three to five recommended, and for role setting focusing behaviour and tone.
- OpenAI Help Center — Best practices for prompt engineering with the OpenAI API— source for the instruction to be specific, descriptive and as detailed as possible about desired context, outcome, length, format and style, for the recommendation to replace vague phrasing such as "fairly short, a few sentences only" with a measurable specification, and for showing the desired structure through examples rather than describing it.
- OpenAI Help Center — Prompt engineering best practices for ChatGPT— source for prompts needing to be clear, specific and to provide enough context for the model to understand the request, for using descriptive adjectives to indicate tone, and for prompting being an iterative process: start, review the response, refine.
- Anthropic — Use examples (multishot prompting)— the detail behind the examples guidance: how many to give, why they should be diverse enough to avoid teaching an unintended pattern, and how to delimit them from the surrounding instructions.
- Anthropic — Giving Claude a role with a system prompt— the mechanism behind role prompting, and the basis for the distinction this article draws between a role that supplies usable perspective and one that supplies adjectives.
- OpenAI — Prompt engineering guide (API documentation)— the developer-facing version of the same guidance, covering instruction clarity, supplying reference material, and splitting complex requests into simpler subtasks rather than overloading a single prompt.
Managed network and communications services, SDVOSB. We are not a prompt-engineering consultancy — we published this because the same staff who ask us about firewalls ask us why the AI keeps giving them useless answers, and the honest answer turned out to be short enough to write down. Support: (888) 989-4872 · support@adampulse.us