So first off, AI prompting sounds futuristic—but the core idea is simple. You type something into ChatGPT, Claude, or any LLM (large language model), and it gives you an answer. But when you’re trying to solve a problem that involves multiple constraints (like planning a trip where everyone has dietary needs, visa requirements, and conflicting vacation days), a vague prompt like “Plan our trip” will give you one-size-fits-all fluff.
Thank you for reading this post, don't forget to subscribe!
The shift happens when you start defining the “thinking environment” in the prompt. Just like telling a friend, “Don’t book any red-eye flights because I have sleep apnea”—good prompts tell the AI what to prioritize, what assumptions are off-limits, and what kind of tradeoffs it’s allowed to consider. This is where cognitive prompts come in. They aren’t just tasks—they’re strategic directions.
Let me give you an immediate, reachable use case. I was helping a friend who was deciding whether to switch to freelancing full time or keep a corporate job. The regular prompt “Should I freelance or keep my job?” got a bland pros-and-cons list.
But we got dramatically better output using this instead:
You're a career strategist specializing in risk-opportunity tradeoffs. Compare the long-term implications of switching to freelance vs staying corporate for someone with moderate savings, ADHD, and no dependents. Output a confidence matrix showing projections under optimistic, realistic, and pessimistic scenarios.
The change was night-and-day. Instead of default advice, it surfaced risk sensitivity, loss aversion, and time management—a huge help given ADHD. The confidence matrix broke down expected income ranges, compared burnout risk, and highlighted which assumptions were influencing the outcomes most. Suddenly, AI felt like a tool for actual strategic thinking, not just a robot assistant.
The bottom line is: if your prompt doesn’t invite deeper reasoning or eliminate irrelevant constraints, it’s not a cognitive prompt yet.
Using Breakdown Prompts to Clarify Problems
This is my go-to when a problem feels bloated or vague. A breakdown prompt does exactly what it sounds like—it tells the AI to deconstruct. Think of it like asking someone, “What do I not see here?”
Take this real scenario: one client couldn’t get new users to onboard properly. They assumed it was the UI design. But we used this breakdown prompt in Claude:
You are acting as a product researcher. The problem is: 'New users create accounts but do not complete onboarding.'
Break this into possible categories: (1) Friction points in UX, (2) Lack of motivation, (3) External distractions, (4) Unclear ROI.
For each category, list 2 candidate sub-causes and what data would validate or disprove them.
This broke their tunnel vision. It suggested things like: maybe users were distracted because of required email confirmations (which came hours later due to send throttling). No UX tweaks would fix that. Another cause was the vague ROI—users didn’t see value without importing more data, but importing wasn’t prompted early on.
Key small detail—it also suggested instrumenting drop-off points with event tracking via Segment, not just Hotjar heatmaps. That hint actually led them to discover the exact screen where most drop-offs happened.
Action Tip: Anytime you feel stuck, frame the situation as a list of competing categories. Tell the AI the angle you want, and add-—”Include an evidence plan” to force it to suggest validation steps.
To sum up, breakdown prompting isn’t about answers—it’s about narrowing your field of fire so the AI stops guessing randomly.
Scenario Simulations: Testing Decisions in Parallel
Scenario prompting is the closest the current AI generation gets to predictive modeling. You’re not just asking, “What should I do?”—you set up multiple forks, tell the AI to act like a strategist or a storyteller, and make it simulate what would happen. It’s surprisingly effective if structured right.
I did this recently when debating whether to launch a leadership training product with a prerecorded video course versus a live 5-week cohort. No audience yet, limited budget, and I wanted to test upside/downside risk.
This was the core prompt:
Assume the role of a go-to-market strategist. Simulate 3 scenarios:
(1) You launch a $300 pre-recorded course using ads
(2) You launch a $900 live cohort with 12 people
(3) You offer 1:1 coaching at $1200/month.
For each, estimate time investment, ideal buyer psychological profiles, marketing channel feasibility, and refund likelihoods. Point out early red flags before launch.
The model didn’t just regurgitate marketing advice. It highlighted that (1) would burn ad spend due to unclear authority (zero testimonials), (3) would scale poorly unless I productized onboarding, and (2) was hard but addressable via partnerships. It even drafted a 3-week go-to-market schedule for the live version including dry runs on Zoom.
More impressively, it flagged that people joining cohort (2) might actually become customers of (3) because of higher perceived intimacy. I hadn’t seen that loop.
Scenario
Time Required
Buyer Profile
Scaling Risk
Pre-recorded
40 hours upfront
Self-paced learners
Low, but high return risk
Live Cohort
10 hours/wk over 5 weeks
Community-focused
Moderate
1:1 Coaching
Variable, but high
Leadership-seeking execs
High time bottleneck
Overall, scenario prompts give you LLMs as simulation engines. The trick is being specific about constraints and matching structure across each fork.
Counterfactual Prompts: What Did We Miss?
Counterfactuals are a little advanced but very useful after a failed decision. A counterfactual is a prompt asking, “What would have happened if we’d chosen differently?”—not for regret, but to analyze missed assumptions.
Let’s say your team just launched a new pricing model and churn spiked. People blamed “confusing UI” or “bad timing” but nobody really knew. Try this in Claude:
You're a postmortem facilitator. Our decision: increased prices and removed free tier.
Effect: churn increased by 30%.
Run 2 counterfactual simulations:
- What might have happened if prices stayed, but premium features were gated better?
- What if new pricing was introduced gradually, segmenting old users from new ones?
List likely reactions, user types affected, and success odds in each.
This causes the LLM to reevaluate the context instead of fixating on what happened. It pulled out—accurately—that legacy users felt punished by “overnight” treatment. It also guessed that gradual segmentation would’ve preserved paid plan conversion while avoiding a loyalty backlash. Spot on.
Takeaway: AI is shockingly good at modeling human reaction if you force it to backtrack and compare forks. Avoid generic blame language—use specific policy shifts and measured outcomes.
To conclude, counterfactual prompts switch the AI from advice-mode into reflection-mode, perfect for digging into screwups.
Chain-of-Thought Prompts for Difficult Logic
This is a newer prompting style that exploded in 2023, and it’s genuinely useful. Chain-of-thought (CoT) prompts force the model to show its thinking step-by-step before giving the final conclusion. Think of it like telling it: don’t skip the work, explain your math on the test.
In one test, I wanted to compare two warehouse layouts using AI. But just asking “Which layout is more efficient?” got junk answers. I switched to a CoT format like this:
Break down evaluation in steps:
1. Define metrics: travel time per item, safety, visibility
2. Use both layouts provided in JSON to simulate worker paths for 3 tasks
3. Compare average travel and congestion points
4. Suggest which layout performs better and why
The AI ended up walking step-by-step through each path. One layout had fewer choke points but worse lighting (flagged under safety). Another was fast but more prone to backtracking. It even wrote very basic Python code simulating picker movement across aisles—primitive but useful directionally.
And when results were borderline, it added: “Layout A may perform ~15% better during low-volume, but Layout B may outperform under rush due to parallelism.” That nuance didn’t show up in normal asking.
In summary, if your prompt involves multiple variables or silent tradeoffs, force chain-of-thought steps explicitly. It slows down hallucinations and gives you breadcrumbs to challenge or test.
Meta Prompting: How to Ask Better Next Time
Finally, there’s meta prompting—asking an LLM how to prompt itself better for a task at hand. I throw this in when my output is stale or generic and I can’t figure out what I’m missing.
Let’s say I’m trying to debug a flaky webhook that fires twice sometimes. Instead of just saying: Why is my webhook sending double responses? —I’m better off asking:
You're a webhook reliability expert. What 3 prompts would help surface root causes of unpredictable double firing? Include systems, rate-limiting, and retries in scope.
The AI might suggest:
“Model webhook behavior assuming at-most-once vs at-least-once delivery. What retry logic could result in duplicates?”
“Given X third-party API, what internal delays or timeouts send multiple callback requests?”
“Simulate split-second failures during Lambda execution. Could a retry be triggered before the timeout is logged?”
Each prompt digs deeper than my first guess. Meta prompting works like a writing coach—it doesn’t solve your problem, it shows how to better frame the question so the next response is clearer, narrower, and more answerable.
To wrap up, whenever I feel like ChatGPT or Claude is just waffling, I pause and feed in a meta prompt for better targeting.
