Simple Prompts vs Long Prompts: Why Simple Questions Get Better AI Answers

You've probably seen both camps by now. One says simple prompts vs long prompts isn't even a contest: short wins, stop writing essays. The other says if your prompt fits in a tweet, you're getting the average internet answer and deserve it. Both have a point, and both are arguing about the wrong thing.

The prompts that get good answers aren't short. They're single-minded. They ask one clear thing, give the model what it needs to answer that thing, and leave out everything else. Word count is a side effect.

Simple prompts vs long prompts is the wrong fight

Read the threads closely and the 'short prompts win' crowd isn't really praising brevity. The r/PromptEngineering post titled 'Stop writing long prompts on Claude' boils its own conclusion down to 'clear intent + constraints > long explanations.' That's not a word limit. That's a rule about what each word is doing.

And the pushback in the same thread lands on the same idea from the other side. One commenter, u/kdee5849, argued that some context genuinely helps, because the answer should be right 'FOR YOU, not for the sort of average human experience.' Context about you is a constraint. It's earning its place. What doesn't earn its place is the stuff people add out of habit: role-play wrappers, polite preamble, three tasks stapled together, and a paragraph explaining why you're asking.

What prompt bloating actually looks like

Another r/PromptEngineering thread gave the problem a name, prompt bloating, and summed it up in one line: 'Prompt engineering is not about writing more.' The fix it proposed wasn't to trim adjectives. It was to stop asking one prompt to do several jobs, and split the work into steps instead: classify, then enrich, then generate.

A DEV Community piece makes the sharpest version of the argument: 'A long prompt is not a precise prompt. It is an ambiguous prompt that happens to have a lot of words in it.' And then: 'Every sentence that does not tightly constrain the output is a sentence that dilutes the sentences that do.' Its worked example takes a product-description prompt of roughly 200 words down to about 40, mostly by deleting sentences that sounded careful but didn't change the output.

Bloat usually shows up in a few recognizable forms:

• A role nobody needed ('You are a world-class senior marketing strategist with 20 years of experience...').
• Stacked tasks: summarize this, then critique it, then rewrite it, then suggest a title.
• Instructions that restate the obvious ('make it clear and well written').
• Backstory about why you need the answer, when it doesn't change what the answer should be.

The evidence is thinner than anyone admits

Here's the honest part most articles skip. Nobody has a rigorous, controlled study showing short prompts beat long ones, or the reverse. The Reddit poster who 'tested 200+ prompts' is self-reporting, and one commenter's whole reply was 'Stop posting nonsense.' The DEV article's mechanism is an analogy, not a measurement.

The most concrete claim comes from a PromptZone write-up of a 61-comment Hacker News thread on the 'just ask Claude' habit. Its core observation: 'many users over-engineer prompts instead of stating their goal plainly to Claude.' Early testers there reported that dropping step-by-step scaffolding and role-play wrappers cut their prompts roughly in half, with the same or better coherence. The article itself admits that's anecdotal. Still, it matches what you'll likely see yourself: plain-language prompts that name the goal tend to get answers aimed at the goal.

A before-and-after: one goal, not fewer words

Here's a typical over-engineered request, the kind that feels thorough while you're writing it:

Before: 'You are an expert financial advisor and educator. I'm 29, I've been thinking about money a lot lately and I want to get my finances in order. Can you explain index funds, tell me whether I should invest or pay off debt first, give me a monthly budget template, and recommend some books? Please be detailed but also concise and easy to understand.'

That's four questions, a role, and two contradictory style notes. The model will answer all of it, thinly. Now strip it back to the one thing you actually need today, and keep only the context that changes the answer:

After: 'I'm 29 with $8,000 in credit card debt at 24% APR and $3,000 in savings. Should I pay off the card or start investing in an index fund first? Give me a recommendation and the reasoning in under 150 words.'

The second prompt is barely shorter. It's just about one thing. The debt numbers stay because they decide the answer; the role and the backstory go because they don't. The budget template and book list can each get their own prompt later.

When a longer prompt earns its length

The counter-view deserves a fair hearing. The AI IQ Substack argues that 'if your prompt fits in a tweet, it is too short,' since without context ChatGPT 'answers like an average internet human.' The author uses long, structured prompts (background, goal, scope) for serious work and short ones for quick questions. That's not a contradiction of the 'one goal' idea. It's what one goal looks like when the goal is big.

Even the 'just ask' write-up concedes plain asking is 'Less reliable on tasks requiring strict output formats' and weaker 'when the user lacks domain knowledge to judge the answer.' So how long should a ChatGPT prompt be? My working rule: add length only when it does one of these jobs.

• The output has a strict format. Show one or two examples of the exact shape you want instead of describing it.
• You don't know the domain well enough to spot a wrong answer. Give background and ask the model to state its assumptions.
• The task is multi-step math or logic. Ask it to reason step by step before answering.
• The answer depends on your specifics: your numbers, your audience, your constraints.

Is prompt engineering dead? Not quite

The 'is prompt engineering dead' posts from Learn Prompting and Chris Lema mostly land in the same sensible middle: for casual questions you can just talk to the model, but specificity still matters for complex or multi-step work, and different models respond differently to the same wording.

That's the real takeaway. Over-engineering prompts is a habit, not a skill. The skill is knowing what your one question is, then giving the model exactly the context that question needs. Sometimes that's eight words. Sometimes it's three paragraphs and two examples. Before you hit send, ask yourself: if I deleted this sentence, would the answer change? If not, delete it.

References

• Reddit r/PromptEngineering - "Stop writing long prompts on Claude. These 5 one-liners outperform most 'perfect prompts' I tested." (mirror)
• Reddit r/PromptEngineering - "Prompt bloating is killing your AI workflows (no one talks about this)" (mirror)
• PromptZone - "'Just Ask Claude' Skill: Why It's Rare and Valuable"
• DEV Community - "Why Long Prompts Make AI Worse and How to Fix Them"
• AI IQ Substack - "If your prompt fits in a tweet, it is too short"
• Learn Prompting - "Is Prompt Engineering Dead?"
• Chris Lema - "Prompt Engineering Isn't Dead"