5 Prompts That Get Dramatically Better Answers Out of Claude

Most people use Claude like a search box, ask a question, take the first answer, move on. The people getting genuinely useful work out of it are doing something different. They are giving it a handful of short, specific instructions that change the kind of answer it returns. Here are five of them, why they work, and the honest limits of each. None are secret, and all of them work on any strong model, but most people never use them. There’s a large gap between the answers Claude gives most people and the answers it is capable of giving, and the gap is almost entirely about how you ask. A model trained to be helpful and agreeable will, by default, give you something pleasant, balanced, and a little shallow, because that’s what a vague prompt rewards. The work happens when you override that default with a specific instruction about what you actually want. What follows are five short prompts that reliably change the quality of what comes back. I’m using Claude as the example because it responds well to this kind of direction, but none of these are Claude-specific tricks, they work on any capable model, and none of them are magic. They are just ways of telling the model to drop its defaults and do the more useful thing. Think of them less as cheat codes and more as the instructions the model was waiting for you to give. /brutal The single most useful instruction, because it fixes the most common failure. By default, a model leans toward encouragement. Ask it to review your business plan, your code, or your writing, and it will lead with what is good, soften every criticism, and leave you feeling better than your work probably deserves. That’s pleasant and nearly useless when you’re trying to improve something. The fix is to explicitly ask for the opposite. Tell it to be brutal, to skip the praise, to act as a harsh critic whose only job is to find what is wrong. A prompt like critique this as harshly as you can, assume it is flawed and find every weakness, no encouragement changes the entire character of the response. Instead of a supportive note, you get the actual problems, the weak argument, the unhandled edge case, the paragraph that does not work. The honest caveat is that it’ll find problems whether or not they are serious, because you told it to, so you have to weigh the critique rather than take every point as gospel. But a harsh review you filter is far more useful than a gentle one that told you nothing. When you genuinely want to make something better, this is the instruction that gets you there. /eli5 Short for explain it like I am five, and it’s the fastest way to actually understand something rather than receive a wall of jargon. Ask a model to explain a complex topic and it often answers at the level of the source material, dense, technical, full of terms that assume you already know the thing you are asking about. You end up with a correct answer you can’t use. Asking for an explanation as if you were five, or for a plain version with no jargon, forces a different mode. The model has to find the simple structure underneath the complexity, reach for an everyday analogy, and drop the vocabulary that was hiding the idea. Explain how a mortgage-backed security works like I am five produces something you will actually remember, where the technical version produces something you will reread three times and still not grasp. The limit is that simplification loses precision, an analogy is never the whole truth, so for anything you need to get exactly right you follow up and ask for the rigorous version once the basic shape is clear. Used as a first step, though, it’s the difference between understanding a thing and merely reading about it. Start simple, then add the detail back. /steelman The instruction that makes a model genuinely useful for thinking, because it forces it to argue against you well. When you ask whether your idea is good, or whether some position is right, the model tends to agree with the framing you handed it, especially if you have signaled which answer you want. That feels validating and teaches you nothing. Steelmanning is the opposite of a straw man. Instead of attacking the weakest version of a position, you ask the model to build the strongest possible version of it. Tell it to steelman the case against your plan, or to make the best argument an intelligent person on the other side would make. Now you are not getting agreement, you are getting the strongest opposition to your own view, which is the only thing that actually tests it. It’s the fastest way to find out whether your idea survives contact with a smart adversary, before a real one finds the hole. The caveat is to use it in both directions, steelman the side you disagree with to understand it, and steelman the side you agree with to see if the strong version holds up. The value is in the discomfort, a good steelman should make you slightly less sure of yourself, and that is the point. /missing Five words, and the closest thing to a blind-spot detector you’ve got. Most prompts ask the model to answer the question you posed. This one asks it to look at the question itself, at the things you did not think to ask, the assumptions baked into your framing, the option you never considered. It works because a model has a broad view of how problems like yours usually go wrong, and your framing is necessarily narrow, shaped by what you already know. Asking what am I missing here, or what would someone with more experience catch that I would not, pulls in the considerations outside your current view. You ask it to plan a product launch, then you ask what you are missing, and it surfaces the legal step, the support load, the dependency you forgot, the thing that was going to bite you in three weeks. The honest limit is that it can produce generic checklists if your context is thin, so the more of your actual situation you give it, the sharper the blind spots it finds. But even the generic version catches things, and the specific version regularly catches the one thing that mattered. It costs five words, and it’s worth asking on every plan you make. /10x The instruction that refuses the first answer, because the first answer is almost never the best one. A model gives you a reasonable response to your prompt and stops, because reasonable is what you asked for. It won’t, on its own, reach for the dramatically better version unless you tell it to. So you tell it to. After the first answer, push it, ask for a version ten times better, or tell it that response was a starting point and you want it to push much harder. This forces the model past its first, safe attempt into more ambitious territory, a bolder structure, a sharper angle, a more creative solution it would not have offered to a prompt that seemed satisfied with ordinary. The same is true going the other way, when an answer is overcomplicated, asking for the version ten times simpler strips it to what actually matters. The caveat is that more ambitious isn’t always better, sometimes the first answer was right and the pushed version is just louder, so you keep what genuinely improved and discard what only got bigger. But the habit of not accepting the first draft, of treating it as the opening move rather than the final word, is most of what separates people who get extraordinary output from people who get ordinary output. The model can almost always do better. You just have to ask. The pattern underneath all five Step back and these five are the same move in different clothes. Each one overrides a default. Be brutal overrides agreeableness. ELI5 overrides jargon. Steelman overrides validation. What am I missing overrides your narrow framing. 10x it overrides the first acceptable answer. A model left to its defaults gives you pleasant, balanced, jargon-tinged, first-draft, on-the-rails answers, because that’s the safe response to an unspecified request. Every one of these prompts is just you specifying the more useful thing instead. That’s the real lesson, and it’s bigger than any single prompt. The quality of what you get out of a model is mostly a function of how clearly you tell it what kind of answer you want. These five are a starting set, not a complete one, and once you see the pattern you will start writing your own. The model is usually capable of far more than its default gives you. These are simply five ways of asking for it. If you have your own prompt that reliably changes the quality of what you get back, drop it in the comments. The genuinely useful ones tend to be short, specific, and a little uncomfortable, and they spread by word of mouth more than any guide. 5 Prompts That Get Dramatically Better Answers Out of Claude was originally published in Towards AI on Medium, where people are continuing the conversation by highlighting and responding to this story.