Prompting for general tasks: common mistakes and how to fix the answer
You paste a report into the chat box and type “summarise this.” Back comes five bland bullet points that could describe almost any report, nothing you can use in the meeting you’re prepping for. Or you type “write me an email to my landlord” and get a stiff, generic note that doesn’t mention the broken heater, the date, or the fact that you’ve already emailed twice. The answer disappoints, and the easy conclusion is that the model isn’t very good. It usually is. The prompt didn’t say enough for any answer to be the right one.
“Summarise this” never told the model who the summary is for, how long it should be, or what you care about. “Write me an email” never said what about, in what tone, with what outcome. A person handed the same instruction would ask follow-up questions. The model doesn’t always ask; it guesses, and an average guess is what you got.
This is the Description skill from the AI fluency framework: saying clearly what you want so the model can act on it (more on the wider framework in the 4D framework for working with AI). The other half is Discernment: judging whether the answer it gave back is right before you use it. Most everyday frustration with AI lives in the gap between those two. This is a field guide to closing it: what a workable prompt is made of, the handful of mistakes that produce most bad answers, and how to repair a response without starting from scratch.
What a prompt that works is made of
A prompt the model can act on has four parts. Not every prompt needs all four, but when an answer disappoints, the missing piece is almost always one of these.
Take the weak prompt and rebuild it. “Summarise this” becomes: “You’re helping me prep for a budget meeting (role and context). Summarise the attached report (the task) as five bullets, each under fifteen words, focused only on cost changes and risks (constraints and format). Here’s the style I want: ‘Cloud spend up 18%, driven by the new logging pipeline’ (example).” The model now has somewhere to stand. The summary comes back about the thing you actually care about, in the shape you can paste straight into your notes.
The same rebuild works on the email. Give it the context (broken heater, two prior emails, you want a repair date), the task (a firm but polite follow-up), the format (short, three sentences), and a line of the tone you want. The point isn’t longer prompts for their own sake. It’s that each part removes one thing the model would otherwise have to guess.
Seven ways prompts go wrong
Across Anthropic’s prompt engineering overview and its interactive tutorial, the same techniques keep appearing as the highest-impact ones: be clear, give context, show examples, let the model reason, name the format, give a role. The common mistakes are just those techniques missing. Here are the seven that produce most bad answers, each with its one-line fix.
- Vague instruction. “Make it better,” but better how? Fix: say exactly what you want, the way you’d brief a new colleague with no background.
- Missing context. The model can’t know your audience, your constraints, or why you’re asking. Fix: state the why, the who, and the limits up front.
- No example. You describe the output instead of showing it. Fix: paste one or two samples of the shape you want.
- No format named. You leave the shape to chance and get a wall of prose. Fix: name it, whether a table, three bullets, or a 200-word summary.
- Too many things at once. One prompt asks for a summary, a translation, and a plan, and each comes out thin. Fix: do one job per prompt, or number the asks.
- A reasoning question with no room to reason. You ask for a judgement but want it instantly. Fix: ask the model to think through the steps before giving the answer, chain-of-thought, which Anthropic recommends for anything analytical.
- Assuming it remembers. You refer to “the document from earlier” it never actually saw, or that scrolled out of context. Fix: re-paste the thing you’re referring to instead of pointing at it.
The seventh mistake isn’t in the prompt at all: trusting the first answer without checking it. That’s the Discernment side of the AI fluency framework: the answer can read fluent and confident and still be wrong, and a polished surface is not evidence.
When the response is wrong, read the symptom
When an answer comes back wrong, the instinct is to rewrite the whole prompt and try again. Usually you don’t need to. The shape of the failure tells you which part of your prompt was thin: read the symptom, trace it to the cause, change one thing.
| Symptom in the answer | Likely cause in your prompt | The fix |
|---|---|---|
| Too generic, could be about anything | No context or example given | Add context and one example of the output you want |
| Made something up (a fact, a quote, a citation) | Asked beyond what the model reliably knows | Give it the source to work from; tell it to say “I don’t know” rather than guess |
| Wrong format: prose when you wanted a table | No format named | Specify the shape: a table with these columns, three bullets, JSON |
| Far too long or too short | No length set | Set a concrete limit: “in 200 words”, “exactly five bullets” |
| Ignored part of what you asked | Too many asks crammed into one sentence | Number the asks, or split them into separate prompts |
| Right facts, wrong tone | No role or voice set | Give it a role: “you’re a careful copy editor”, “write it warmly” |
| Refused or hedged when it shouldn’t have | A constraint read as off-limits, or the ask was ambiguous | Rephrase the constraint and say why you’re asking and what it’s for |
The made-something-up row is worth sitting with, because it’s the one that quietly costs people. A model fills gaps with plausible text, so a question it can’t answer from what it knows produces a confident invention rather than a blank. Giving it the source to quote from, and explicitly permitting “I don’t know,” turns a guess back into an honest gap.
Steering in follow-ups
You almost never need a fresh prompt. The chat already has the context, the draft, and your earlier instructions. Correcting in place is faster and keeps all of that. When an answer is 80% there, say what to change: “Good, but tighten it to three bullets and keep the second point exactly as is.” Or: “Same email, but drop the apology in the first line and make the deadline firmer.” You’re steering a draft, not commissioning a new one.
This is the Description and Discernment loop running live: you judge the answer (Discernment), you describe the adjustment (Description), the answer improves, you judge again. A few short turns usually beat one long prompt, because you’re reacting to a real output instead of trying to predict every requirement in advance.
Before you hit enter
A quick pass before you send, and a habit for after. Most of the seven mistakes get caught here.
- Is the ask clear? Would a new colleague know exactly what to do from this sentence alone?
- Did I give context? Who it’s for, why, and any constraints the model can’t know.
- Is there an example? For anything where format or style matters, show one.
- Did I name the format and length? The shape (table, bullets, prose) and the size.
- One job or many? If it’s many, number them or split them.
- Will I actually check the answer? Decide before you read it that you’ll verify the facts, not just the fluency.
That last item is the Discernment habit, and it matters most when you’ll act on the output. The case for checking what you ship, rather than trusting a confident-looking result, is the whole argument of vibe coding versus engineering: the output looking right is not the same as it being right. And if you want to get better at this steadily rather than picking it up by osmosis, becoming AI-fluent: a learner’s playbook is the longer path. Anthropic’s own foundations course frames the same two skills as the base everything else builds on.
Good prompting isn’t a trick or a secret phrase. It’s the ordinary discipline of saying what you want clearly, showing an example when shape matters, and reading the answer back critically, then adjusting in place instead of starting over. Do that, and most of the answers that used to disappoint stop disappointing.
Cited sources