AI Central's ChatGPT-5 Prompts For Problem Solving does one useful thing. It stops you asking a chatbot for advice and starts you making it run a named method. Twenty numbered prompts, each locked to an established framework, from the 5 Whys and the Fishbone diagram to Kepner-Tregoe scoring, a pre-mortem and the Minto Pyramid. Every one opens with your problem in full context and closes with the exact output shape you want back. That envelope is the real technique.
Reviewed August 2026.
Why naming the framework changes the answer
Ask a model how to fix a stubborn problem and you get plausible general advice, because a general question earns a general answer. AI Central's problem-solving prompts never ask for advice. Each names a procedure, then hands the model the slots that procedure requires.
The Fishbone prompt is the clearest case. It names six categories of cause, People, Process, Equipment, Environment, Materials and Management, and demands at least two specific, plausible root causes inside every one of them. That is twelve forced hypotheses instead of the three convenient ones a model offers unprompted. It also asks for the result as a text-based diagram, so the categories stay separate rather than collapsing into a paragraph of hedging.
The Kepner-Tregoe prompt does the same to decisions. It splits your criteria into Musts, which are non-negotiable, and Wants, which are merely desirable, asks for weights and scores against each alternative, then requires a tabulated result and a plan to mitigate the risk attached to the winner. There is no room left for it depends.
The envelope every prompt shares
Twenty prompts, one wrapper. AI Central opens every one with a bracket asking you to mention the problem you are facing in detail with background context, and closes every one with a bracket asking you to say how you want the output, in detail, with examples. Those two slots are not decoration. They are the part most people skip and the part that decides the quality of what comes back.
The front slot exists because a framework is only as good as the facts poured into it. A 5 Whys chain built on one sentence of context invents its own middle layers and sounds confident doing it. The back slot exists because these frameworks all produce structured output, a matrix, a table, a diagram, a ranked plan, and structure is the first thing a model drops when nobody asks for it.
- Front slot: the whole situation, including what you already tried and what happened when you tried it.
- Middle brackets: the industry, the domain, the list of options, the hypothesis. Leave one empty and the model fills it with a guess.
- Back slot: the output shape you want, plus an example of what good looks like.
The diagnostic prompts, and why they refuse symptoms
Four of the prompts exist purely to find causes. The 5 Whys chain, the Fishbone map, a hypothesis validation plan and an after-action review. All four share a bias against the easy answer. AI Central's 5 Whys prompt asks the model to summarise each layer of cause and its relationship to the layer above, then ends with a blunt instruction.
Conclude with actionable recommendations to address the root cause, not just the symptoms.
The hypothesis prompt goes further and assumes you might be wrong. It asks the model to define what evidence would confirm or refute your suspicion, suggest how to collect it, explain how to read the results, and then, if the hypothesis does not survive, help you generate and test alternatives. That last clause is the difference between analysis and confirmation bias.
The after-action review prompt is the retrospective version. What was supposed to happen, what actually happened, why the two diverged, then strengths to sustain and weaknesses to fix. Built to produce lessons rather than blame.
The two prompts that make the model interview you
The most transferable ideas here are the two that invert the conversation. AI Central's flipped interaction prompt tells the model not to answer at all, and instead to ask targeted questions about the details, the context and the outcomes you want. The instruction on sequencing is explicit.
Only after I have answered all your questions should you synthesize the information and provide a comprehensive, step-by-step solution tailored to my specific situation.
The cognitive verifier prompt is the lighter version of the same move. Whenever you ask a question, the model must first generate three additional questions whose answers would let it respond more accurately, present them, wait, and only then combine everything into a final solution. Three questions is small enough to bolt onto almost any prompt you write, which is why it is the one to steal.
Both work for the same reason. A model given a thin question fills the gaps with the statistical average of everything it has read, which sounds competent and fits nobody. Forcing the questions out first replaces that average with your actual situation.
Planning when the facts run out
A second cluster handles decisions where the evidence is genuinely incomplete. AI Central's scenario planning prompt asks for three futures, optimistic, pessimistic and realistic, each with its own drivers, assumptions and proactive strategy. The Ansoft Matrix prompt works four growth quadrants and forces a risk, resource and reward assessment on each before recommending any. The first principles prompt strips a problem to its fundamental components, questions every assumption, rebuilds from the ground up, then compares that answer against the conventional one.
The pre-mortem is the sharpest of them and the most underused technique on the list. Instead of asking what could go wrong, it instructs the model to assume the thing already did.
Perform a Pre-Mortem Analysis: imagine it is six months from now, and the initiative has failed spectacularly.
The tense change is the trick. Asking what the risks are produces a hedged list. Telling the model it failed and asking why produces specifics, because it is now describing an event rather than speculating about one. The prompt then asks for a preventive measure or contingency plan against each reason, which turns the pessimism into a checklist.
Arguing, aligning and shipping
The last group deals with what happens after you have an answer, which is where most analysis quietly dies. AI Central's Minto Pyramid prompt makes the case top down, recommendation first, then supporting arguments with evidence attached, ending by anticipating objections. The Delphi prompt handles a divided team by summarising the disagreement, then running anonymous iterative rounds toward consensus. The S.M.A.R.T. prompt converts a decision into milestones, metrics and contingencies. User journey mapping walks an experience touchpoint by touchpoint, including the emotional state at each one, hunting the moments of truth where it can be fixed.
How to actually run these
- Pick by the shape of the problem, not the elegance of the framework. Recurring and unexplained means the 5 Whys or Fishbone. Overloaded means Eisenhower. A choice between named options means Kepner-Tregoe.
- Fill every bracket before you send. An unfilled bracket is a licence to guess.
- If you are unsure which framework fits, run the cognitive verifier pattern first and let the model ask its three questions.
- Chain them. Scenario plan, then pre-mortem the plan. Diagnose, then set a S.M.A.R.T. goal against the cause.
- Keep your filled-in versions. The brackets turn each prompt into a personal template the moment you write your context in properly once.
AI Central's own closing note puts it fairly. The prompts are the resource, the method is the engine, and the method here is refusing to let a model answer in generalities.
Do these prompts only work with ChatGPT-5?
Nothing in the wording depends on a model-specific feature. Every one is plain instruction: name a framework, supply context, specify the output format. AI Central wrote them for ChatGPT-5, and the structure carries to any capable assistant, though the reasoning quality inside the framework will vary.
How much detail do I really need to put in the brackets?
More than feels natural. What the problem is, how long it has been running, what you already tried, what happened when you did. Every framework here derives its output from that input, so a thin brief produces a confident, tidy and useless matrix.
Which one should I start with?
The 5 Whys prompt if the problem keeps coming back. The Eisenhower prompt if you are simply overloaded and cannot tell what matters. The pre-mortem if you are about to commit to something you cannot easily undo. Those three cover most of what people actually bring to a model.
What if the model still comes back with generic advice?
Check the closing bracket first. That failure usually traces back to an unspecified output format. Ask for the table, the text-based diagram, the ranked list, the weighted scores, show an example of the shape you want, and the vagueness tends to go.