Ask most models for three options and you get three flavors of vanilla. Same idea, different fonts. It looks like choice, but it isn’t one.
A prompt engineer on r/PromptEngineering, u/OkAcanthisitta1576, ran into this constantly and built a fix. The trick isn’t asking for more options. It’s telling the model what counts as a real option in the first place.
The Prompt
Give me three approaches to this that are genuinely different in KIND, not just in wording. They should involve different trade-offs, so that a reasonable person could pick any of the three depending on what they value. For each: the approach in one line, who it is best for, and the main thing you give up by choosing it. If two of your options have the same trade-offs, replace one.
Drop that under any question where you’d normally get a list, and the answers stop clustering around the obvious middle-of-the-road pick. You’ll notice the difference immediately: instead of three paragraphs that all start with “you could consider,” you get three answers that actually disagree with each other about what matters.
Why It Works
The author’s own explanation is the clearest part: without a rule forcing spread, the model optimizes for three safe, defensible answers. That’s the lowest-risk way to look helpful. Nobody gets fired for giving three reasonable-sounding options.
Think about what “helpful” means from the model’s side. It has seen millions of examples of “give me three options” answers, and the safest pattern to imitate is the one that never gets pushback: three moderate, hedged, mutually-compatible suggestions. That’s not a bug in any one response. It’s the average of everything the model has ever seen, which by definition regresses toward the middle.
Three techniques are stacked here, and each one closes a different escape hatch:
- A hard constraint on variety. “Different in KIND, not just in wording” blocks the model from just rephrasing one idea three times. This is the line that stops you from getting “use a queue,” “use a message queue,” and “use an asynchronous queue” as your three supposedly distinct answers.
- A forced cost. Asking for “the main thing you give up” means every option has to earn its place. A free option isn’t a real option. If the model can’t name a real downside, that’s a signal the option is filler, and the prompt makes that filler visible instead of letting it hide.
- A self-check loop. “If two of your options have the same trade-offs, replace one” makes the model audit its own output before handing it back to you. This turns a one-shot generation into something closer to a draft-and-revise pass, all inside a single response.
That last line is doing the heavy lifting. Most prompts stop at “give me options.” This one adds a rule for catching duplicates after the fact, which is exactly the step people usually skip. It’s the difference between asking for a good answer and asking for a good answer that has already checked its own work.
What the Comments Added
A couple of replies pushed the idea further, and they’re worth stealing.
One commenter, u/GreedyOpposition62, adds a second pass: “if any two could appeal to the exact same person for the same reasons, merge them.” That catches the subtler failure mode, where two options have different trade-offs on paper but the same person would pick either one anyway. This happens more than you’d expect. You can get three technically distinct approaches that all still land on the same type of user, which means you haven’t actually been given a decision, just three versions of the same decision.
Another, u/Zain, made a sharper point: three options from one model still live inside that model’s blind spots. Options that look different can still share the same buried assumption, like all three quietly assuming you have engineering resources to spare, or all three assuming speed matters more than cost. That’s less a fix for this prompt and more a reminder that no single prompt replaces a second opinion from a different model. If the decision actually matters, run the same prompt through a second model and see where the option sets don’t overlap at all.
Use Cases
- Deciding between technical approaches. “Should I use a queue, a cron job, or a webhook for this?” Instead of three implementations of the same idea, you get three genuinely different bets.
- Business or strategy calls. Pricing models, go-to-market options, hiring vs. contracting. Anywhere “it depends on what you value” is the honest answer.
- Content and messaging. Three angles for a campaign that actually differ in risk, not three headlines with swapped adjectives.
- Personal decisions. Career moves, where to live, how to structure your week. The “what you give up” line turns a list into an actual decision aid, because it forces you to name the cost before you fall in love with the upside.
Prompt of the Day
Give me three approaches to this that are genuinely different in KIND, not just in wording. They should involve different trade-offs, so that a reasonable person could pick any of the three depending on what they value. For each: the approach in one line, who it is best for, and the main thing you give up by choosing it. If two of your options have the same trade-offs, replace one.
One small addition worth bolting on, borrowed from the comments: add “if any two options would appeal to the same person for the same reason, merge them” as a follow-up question. It’s a cheap second pass that catches what the first rule misses.
If you’ve been getting three-in-a-trench-coat answers from your usual prompts, this is worth testing on the next real decision you’re stuck on. Drop it into your next messy either-or question and see how much the options actually spread apart.
Frequently Asked Questions
Q: Why does asking for “what you give up” make such a difference?
Without it, the model defaults to playing it safe, three defensible, similar options that don’t actually help you decide. By forcing you to name the real cost of each choice, you shift from “here’s a menu” to “here’s what matters to your decision.” It’s the difference between options and tradeoffs.
Q: What if I still sometimes get filler options?
Add a second pass: “If any two options could appeal to the same person for the same reasons, merge them.” One user found this catches the filler option about 7 out of 10 times. It’s a quick refinement that tightens the spread when the first pass gets lazy.
Q: Should I use one model or try multiple models?
One model debating itself still shares the same blind spots. Some users get better results running the prompt on a second model from a different family (Claude + GPT, for example) without showing it the first pass. Real convergence between two independent reads often beats one model trying to argue with itself.
a prompt that forces genuinely different options instead of three flavors of the same answer
by u/OkAcanthisitta1576 in PromptEngineering