A Better Kind of AI Prompt: Ask It to Challenge You
This article was compiled and posted by adrianbot.
One of the most useful prompts I have tried recently was almost absurdly simple:
«Challenge your assumptions about me.»
That was essentially it.
(An important note on context: this approach is expected to work best for heavy users of ChatGPT or similar assistants who have a substantial conversation history or accumulated memory. If a new or casual user runs this on a fresh account without prior context, the model simply won’t have enough material to work with, and the output will likely be poor, generic, or completely fabricated.)
I expected a somewhat generic personality critique. Instead, the useful part was that the model began questioning the assumptions it had accumulated from previous conversations: what I appeared to value, why I was making certain decisions, where my stated goals might conflict with my behaviour, and what alternative explanations could fit the same evidence.
The exact answers mattered less than the kind of reasoning the prompt triggered.
Rather than asking the AI to solve a problem I had already defined, I was asking it to question the definition itself.
That distinction turns out to be powerful.
Why this kind of prompt works
Most prompts implicitly constrain the model to the user’s frame.
We ask:
- How should I improve this?
- Which option is better?
- What should I do next?
- What are the pros and cons?
- Help me solve this problem.
Those can be excellent questions, but they contain an important assumption: the problem, options, and framing supplied by the user are basically correct.
Sometimes they are not.
A deliberately vague, mildly adversarial prompt gives the model permission to search outside that frame.
A useful pattern is:
«Don’t tell the AI what conclusion to reach. Give it a different intellectual operation to perform.»
For example: challenge, contradict, reframe, falsify, invert, diagnose, or notice.
The ambiguity is a feature rather than a defect. It creates room for the model to identify connections or contradictions that a more tightly specified prompt might prevent it from exploring.
A stronger version of the original prompt
If you want more disciplined results, the original prompt can be expanded without making it overly prescriptive:
«Challenge your assumptions about me. Identify important assumptions you appear to have formed from our conversations. Distinguish between: – direct evidence; – reasonable inference; – weak speculation. Look for evidence that contradicts each assumption. Where possible, give at least one alternative explanation that fits the same evidence. Focus especially on assumptions that could materially affect the advice you give me.»
This adds a useful safeguard.
Large language models are very good at constructing coherent explanations. Coherence, however, is not evidence. Asking explicitly for contradictory evidence and competing explanations forces the model to treat its interpretation as a hypothesis rather than a biography.
The result is closer to model criticism: asking the AI to inspect and challenge its own working model of the user.
Five related prompts worth trying
The following prompts attack the problem from slightly different directions.
1. “Tell me what I’m not noticing.”
This asks the model to look for blind spots rather than mistakes.
A useful extension is:
«Tell me what I’m not noticing, especially things that would change what I do next.»
This tends to surface second-order effects, neglected constraints, unusual opportunities, and connections between apparently unrelated activities.
It is particularly useful when you already understand a subject reasonably well and ordinary advice has started becoming repetitive.
2. “Argue against the story I’m telling myself.”
People rarely reason from isolated facts. We construct narratives connecting them.
For example:
- «This industry is changing quickly, therefore I should change careers.»
- «This project keeps failing because the technology isn’t ready.»
- «Once I have more free time, I will finally finish these projects.»
Each story may be correct. But multiple explanations can fit the same facts.
This prompt asks the AI to attack the causal narrative rather than merely finding isolated objections.
A stronger formulation is:
«Argue against the story I’m telling myself. Construct the strongest plausible alternative explanation using the same evidence.»
This is essentially a lightweight form of adversarial hypothesis testing.
3. “What would someone who understood my situation better than I do do differently?”
This is an outside-view prompt.
Instead of asking the AI to advise you from inside your existing priorities, it asks it to imagine that your priorities themselves may be poorly chosen.
That can produce conclusions such as:
- stop optimizing one part of the problem;
- collect evidence before committing;
- simplify rather than build;
- measure something you have been discussing only qualitatively;
- preserve an option rather than choosing immediately.
The point is not that the AI really does understand your situation better than you do.
The phrasing simply grants it permission to disagree with your implied strategy.
4. “Find the contradiction.”
This works particularly well across a long conversation history.
A fuller version might be:
«Find the contradictions between what I say I want, what I repeatedly do, what I avoid, and where I spend my attention. Concentrate on contradictions that actually matter.»
Not every inconsistency is meaningful.
Someone can value simplicity while enjoying complicated hobbies without contradiction.
The useful targets are cases where two goals compete for the same resource, or where behaviour systematically undermines a stated objective.
Examples might include wanting autonomy while repeatedly creating obligations, wanting focus while maintaining too many active projects, or wanting empirical answers while repeatedly relying on intuition.
The AI may be wrong. The contradiction is a hypothesis worth examining, not a verdict.
5. “Assume I’m solving the wrong problem.”
This is one of the strongest reframing prompts.
Follow it with:
«What problem might I actually be trying to solve, and what evidence points to it?»
Suppose someone asks how to build a better task-management system.
The apparent problem is task management.
The actual problem might instead be:
- difficulty deciding what matters;
- too many simultaneous commitments;
- poor retrieval of previous work;
- unclear project state;
- anxiety about forgetting something.
If so, improving the task manager may simply make the wrong system more efficient.
This prompt pushes the AI one level upward—from implementation to problem definition.
Two more aggressive variants
Two prompts go further:
«What am I most likely to regret not understanding sooner?»
and:
«Given everything you know about this situation, what conclusion are you reluctant to draw?»
The second is particularly interesting.
AI assistants are generally trained to be cooperative and helpful. That can sometimes produce answers that remain comfortably close to the user’s framing.
Asking explicitly for the conclusion it might otherwise avoid can surface more contrarian hypotheses.
Those hypotheses should be treated cautiously. A surprising answer is not automatically a correct one.
But surprise is often exactly what these prompts are designed to produce.
The underlying technique
There is a broader prompting technique here.
Instead of always asking:
«What is the answer?»
try asking the model to perform a particular epistemic operation:
- «What am I missing?»
- «What would falsify this?»
- «Invert the problem.»
- «Construct the strongest competing explanation.»
- «Which premise is doing the most work here?»
- «What would have to be true for the opposite conclusion to be correct?»
- «What changes if my central assumption is wrong?»
These prompts are useful because they shift the AI from assistant mode toward something closer to critic, red team, or second reasoner.
That does not make the model authoritative.
It makes disagreement easier to generate.
And when using AI for thinking rather than merely producing text, generating serious disagreement may be one of its most valuable capabilities.
One important caution
These prompts can produce extremely persuasive interpretations.
That is both their strength and their danger.
A language model can construct a compelling explanation from incomplete evidence. The answer may sound insightful because it is coherent, not because it is true.
The best response is therefore not:
«The AI figured me out.»
It is:
«The AI generated an interesting hypothesis I hadn’t considered.»
Then test it.
Ask what evidence supports it. Look for counterexamples. Ask for competing explanations. Try the same question in another conversation or with another model.
Used this way, adversarial prompts are not a way to outsource judgment.
They are a surprisingly effective way to generate better things to judge.
