How cognitive frameworks, reasoning lenses and prompt chaining improve the quality of LLM outputs drasticaly

Help your LLM think

Most people prompt language models by describing the output they want:

This often works, but the result is usually generic.

The model tries to satisfy the visible request, but it has little guidance about how to think.

A more powerful way to prompt is to give the model not only a task, but a thinking pattern.

A thinking pattern is a structured mental model, reasoning lens, diagnostic frame, or decision method that changes how a problem is approached.

Instead of saying:

“Analyse our AI adoption problem.”

You say:

“Analyse our AI adoption problem using Systems Thinking, Jobs To Be Done, Goodhart’s Law, and Theory of Constraints.”

The difference is not cosmetic. You are no longer only asking for an answer. You are asking the model to process the topic through specific cognitive lenses.

This produces outputs that are usually:

• less generic
• more structured
• more diagnostic
• more strategic
• more aware of trade-offs
• more useful for decision-making
• less trapped in obvious “best practice” responses

In practice, thinking patterns turn a prompt from a request into a reasoning architecture.

1. What is a thinking pattern in a prompt?

A thinking pattern is a reusable way of looking at reality.

Pattern	Core question
First Principles	What is fundamentally true?
JTBD	What progress is the person trying to make?
Systems Thinking	What loops, delays, and interactions are at work?
Theory of Constraints	What bottleneck limits the whole system?
Goodhart’s Law	What happens if the metric becomes the target?
Game Theory	How will actors behave based on incentives?
Pre-mortem	Why might this fail?
Bayesian Thinking	How should confidence change with new evidence?

Each pattern asks a different type of question.

A thinking pattern does not guarantee truth. It gives the model a more precise path for producing an answer.

2. What thinking patterns do concretely in an LLM prompt

A language model does not “understand” a thinking pattern the way a human expert does. It does not open a mental drawer called “Systems Thinking” and reason like a trained systems theorist.

More concretely, a thinking pattern in a prompt does several things.

2.1 It activates a learned semantic region

LLMs are trained on vast amounts of text. They have seen many examples of terms like “First Principles,” “SWOT,” “Jobs To Be Done,” “OODA Loop,” “Goodhart’s Law,” or “Theory of Constraints.”

When you include one of these terms, you increase the probability that the model will generate language, structures, examples, and reasoning moves associated with that concept.

For example:

“Analyse our project delays.”

This activates a broad generic business-analysis space.

But:

“Analyse our project delays using Theory of Constraints.”

This activates a more specific space around bottlenecks, system throughput, local optimization, constraints, and leverage.

The topic changes from “general analysis” to “constraint diagnosis.”

2.2 It reduces ambiguity

A vague prompt leaves the model with many possible directions.

Example:

“Improve our AI strategy.”

The model could discuss tools, culture, governance, training, security, change management, ROI, use cases, or technology trends.

But:

“Improve our AI strategy using First Principles, Goodhart’s Law, and Risk Matrix.”

Now the model knows that you want:

• fundamentals
• suspicious treatment of metrics
• structured risk evaluation

The answer becomes more constrained and more likely to match your intention.

2.3 It changes the evaluation criteria

A thinking pattern tells the model what “good” means.

Without a pattern, “good” may mean:

• fluent
• complete
• plausible
• balanced
• conventional

With a pattern, “good” becomes more specific.

For example:

• With Inversion, a good answer identifies failure paths.
• With JTBD, a good answer explains human progress.
• With Goodhart’s Law, a good answer questions metrics.
• With Theory of Constraints, a good answer finds the bottleneck.
• With Wardley Mapping, a good answer distinguishes custom capabilities from commodities.

This is powerful because many weak AI outputs are not wrong; they are just optimized for the wrong kind of quality.

2.4 It creates reasoning scaffolding

Thinking patterns provide an internal structure for the response.

For example, asking for:

“Analyse this through Cybernetics.”

Naturally suggests categories like:

• signals
• feedback
• control
• correction
• adaptation
• learning loops

This prevents the model from simply producing a list of disconnected observations.

The pattern becomes a scaffold.

2.5 It improves decomposition

Many problems are too large to solve in one direct answer.

Thinking patterns break the problem into manageable sub-questions.

Example:

“Why is our AI adoption slow?”

A generic answer may say:

• lack of training
• unclear use cases
• fear
• poor tooling
• no governance

A multi-pattern prompt can decompose the same problem:

• JTBD: What job are users hiring AI to do?
• Systems Thinking: What loops reinforce non-adoption?
• Theory of Constraints: What bottleneck blocks usage?
• Goodhart’s Law: What metric may be misleading?
• Game Theory: Who has incentives to resist?

The model is no longer trying to answer one huge question. It is answering several sharper questions.

2.6 It increases contrast

A good thinking pattern creates a productive “before vs after.”

For example:

• Occam’s Razor fights unnecessary complexity.
• Chesterton’s Fence fights reckless simplification.
• Goodhart’s Law fights naive metric obsession.
• North Star Metric fights metric fragmentation.
• MVP fights overbuilding.
• Risk Matrix fights vague anxiety.

Each pattern pushes against a specific failure mode.

This is why thinking patterns are especially useful in prompts: they make the model less average.

2.7 It helps the model avoid generic best-practice answers

LLMs are very good at producing plausible consulting language. This is useful, but dangerous.

A generic AI output often says:

• align stakeholders
• define goals
• train teams
• create governance
• measure progress
• iterate continuously

None of this is wrong. But it is rarely enough.

Thinking patterns force the model to go beyond generic advice.

Example:

“Use Goodhart’s Law to critique this KPI system.”

This does not invite a generic KPI answer. It invites suspicion about metric gaming, perverse incentives, and proxy failure.

That is a much stronger output.

2.8 It does not change the model’s weights

A prompt does not retrain the model.

It does not modify its memory, parameters, or underlying capabilities.

It temporarily shapes the model’s behavior inside the current context window.

So a thinking pattern is not a software plugin. It is a contextual steering device.

Its effect depends on:

• the model’s prior training
• the clarity of the prompt
• the specificity of the task
• the quality of the input data
• the number of patterns used
• the order in which they are chained
• whether the model is asked to compare, critique, or synthesize

3. Why thinking patterns are especially powerful with LLMs

Thinking patterns work well with LLMs because language models are pattern-completion systems.

They are sensitive to:

• terminology
• structure
• examples
• roles
• constraints
• evaluation criteria
• formatting
• reasoning sequences

When you provide a thinking pattern, you are not only giving a topic. You are giving a processing instruction.

Compare:

“Give me a strategy for AI adoption.”

With:

“Analyse AI adoption using: 1. First Principles

2. Jobs To Be Done

3. Systems Thinking

4. Goodhart’s Law

5. Theory of Constraints

For each lens, explain: - what it reveals - what it hides - how it changes the recommendation - what action becomes obvious”

The second prompt is much stronger because it specifies:

• the lenses
• the sequence
• the expected comparison
• the output structure
• the decision logic

It gives the model a reasoning workflow.

4. The basic formula for using thinking patterns in prompts

A strong thinking-pattern prompt usually has this structure:

Analyse [topic/problem/decision] using [thinking pattern].

For this pattern, provide:
1. What it reveals
2. What it hides
3. What assumptions it challenges
4. What risks it exposes
5. What action becomes obvious
6. A before/after recommendation

For multiple patterns:

Analyse [topic] using the following lenses:
1. [Pattern 1]
2. [Pattern 2]
3. [Pattern 3]

For each lens:
- what it reveals
- what it hides
- what it changes in the recommendation
- what action becomes obvious

Then synthesize:
- where the patterns agree
- where they contradict each other
- what the strongest combined recommendation is
- what should be tested first

The synthesis step is crucial. Without it, the model may produce separate mini-analyses. With synthesis, it must integrate them.

5. Before/after examples for each thinking pattern

Below is a practical catalogue of thinking patterns and how they change prompts.

Each pattern includes:

• what it changes
• weak prompt
• stronger prompt
• why it improves the output

5.1 First Principles

What it changes

Moves from best practices to fundamentals.

Weak prompt

Create an AI training program for employees.

Stronger prompt

Design an AI training program using First Principles. Start by identifying the repeated painful tasks employees perform, the constraints they face, the minimum AI capability needed, and the fastest visible gain.

Why it improves the output

The model stops copying standard training-program advice and starts from the actual mechanism of value creation.

5.2 Cybernetics / Feedback Loops

What it changes

Turns a static plan into a learning system.

Weak prompt

Launch an AI adoption program.

Stronger prompt

Design an AI adoption program using Cybernetics. Identify the signals to observe, the feedback loops to create, the correction mechanisms, and how the system learns from usage.

Why it improves the output

The output becomes adaptive instead of linear.

5.3 Jobs To Be Done

What it changes

Moves from features to human progress.

Weak prompt

Build a prioritization dashboard.

Stronger prompt

Use Jobs To Be Done to define the real job behind a prioritization dashboard. What progress is the manager trying to make in the moment of decision?

Why it improves the output

The model focuses on the user’s situation, struggle, and desired progress instead of jumping to features.

5.4 Ashby’s Law of Requisite Variety

What it changes

Tests whether the solution has enough complexity for the problem.

Weak prompt

Create one AI policy for the company.

Stronger prompt

Design an AI policy using Ashby’s Law of Requisite Variety. Segment rules by risk level, data sensitivity, autonomy level, department, and use-case type.

Why it improves the output

The answer avoids oversimplified governance.

5.5 Good Regulator Theorem

What it changes

Forces modelling before automation.

Weak prompt

Automate our Airbnb operations.

Stronger prompt

Use the Good Regulator Theorem to identify what must be modelled before automating Airbnb operations: owners, listings, bookings, calendar blocks, cleaning rules, exceptions, and sync states.

Why it improves the output

The model recognizes that automation without a system model creates fragility.

5.6 Goodhart’s Law

What it changes

Makes metrics suspicious.

Weak prompt

Track the number of AI prompts used by employees.

Stronger prompt

Critique this AI adoption KPI using Goodhart’s Law. Explain how “number of prompts used” could become misleading and propose better metrics for value, reuse, quality, and time saved.

Why it improves the output

The model stops treating measurement as neutral and starts looking for metric gaming.

5.7 Leverage Points

What it changes

Prioritizes deep interventions over surface fixes.

Weak prompt

Improve our project templates.

Stronger prompt

Analyse our project governance using Leverage Points. Identify which small changes could produce disproportionate effects: criteria for launching, stopping rules, decision rights, incentives, or feedback loops.

Why it improves the output

The model looks for systemic intervention points, not cosmetic fixes.

5.8 Viable System Model

What it changes

Diagnoses the organization as an adaptive system.

Weak prompt

Our product team is slow. How can we improve it?

Stronger prompt

Diagnose our product organization using the Viable System Model. Distinguish operations, coordination, control, intelligence, and policy. Identify which function is weak.

Why it improves the output

The model avoids blaming one team and examines missing organizational functions.

5.9 Inversion

What it changes

Finds failure paths before success paths.

Weak prompt

How do we make this AI tool successful?

Stronger prompt

Use Inversion. Assume this AI tool failed after six months. List the most likely causes, then design safeguards against each one.

Why it improves the output

The model becomes more realistic about failure modes.

5.10 Theory of Constraints

What it changes

Focuses on the bottleneck.

Weak prompt

Improve UX, marketing, sales, and onboarding.

Stronger prompt

Use Theory of Constraints to identify the single bottleneck limiting adoption. Explain why improving non-bottleneck areas may not improve total throughput.

Why it improves the output

The output becomes more focused and less scattered.

5.11 Systems Thinking

What it changes

Replaces linear cause-effect with loops and interactions.

Weak prompt

People resist AI because they dislike change.

Stronger prompt

Analyse AI resistance using Systems Thinking. Identify reinforcing loops, balancing loops, delays, unintended consequences, and hidden dependencies.

Why it improves the output

The model sees adoption as a system, not a personality problem.

5.12 Second-Order Cybernetics

What it changes

Adds the observer into the system.

Weak prompt

Employees are not adopting AI.

Stronger prompt

Use Second-Order Cybernetics to analyse how leadership messaging, measurement, and intervention style may be influencing the AI adoption problem they are trying to observe.

Why it improves the output

The model includes the analyst, leader, or consultant as part of the system.

5.13 OODA Loop

What it changes

Looks at decision speed and adaptation.

Weak prompt

Competitors are faster than us.

Stronger prompt

Analyse our competitive slowness using the OODA Loop: Observe, Orient, Decide, Act. Identify where the loop is slow, distorted, or disconnected.

Why it improves the output

The model locates speed problems in the decision cycle.

5.14 Lean / Kaizen

What it changes

Reduces waste and friction.

Weak prompt

Add another approval step to improve quality.

Stronger prompt

Use Lean and Kaizen to improve quality without adding waste. Identify rework, waiting time, handoff friction, unclear briefs, and unnecessary approvals.

Why it improves the output

The model looks for continuous improvement and flow instead of bureaucracy.

5.15 TRIZ

What it changes

Turns contradictions into innovation triggers.

Weak prompt

We need more control but less bureaucracy.

Stronger prompt

Use TRIZ to resolve this contradiction: we need more control and less bureaucracy. Propose mechanisms that increase transparency, automation, or self-service instead of adding meetings.

Why it improves the output

The model treats tension as an innovation opportunity.

5.16 Design Thinking

What it changes

Centers lived user experience.

Weak prompt

Users need a dashboard.

Stronger prompt

Use Design Thinking to explore the user context before proposing a dashboard. What do users experience, need, fear, and decide in the moment?

Why it improves the output

The model starts from user reality instead of solution assumptions.

5.17 Wardley Mapping

What it changes

Changes strategic positioning of capabilities.

Weak prompt

Should we build our own AI infrastructure?

Stronger prompt

Use Wardley Mapping to decide what we should build, buy, or outsource. Map each capability by user value and maturity: custom, emerging, productized, or commodity.

Why it improves the output

The model distinguishes strategic differentiation from commodity infrastructure.

5.18 Red Teaming

What it changes

Actively attacks the idea.

Weak prompt

Review this strategy.

Stronger prompt

Red-team this strategy. Identify false assumptions, abuse cases, reputational risks, security issues, stakeholder objections, and failure scenarios.

Why it improves the output

The model becomes adversarial and more useful for stress-testing.

5.19 Pre-mortem

What it changes

Makes risks concrete before execution.

Weak prompt

Let’s launch in six months.

Stronger prompt

Run a pre-mortem. Imagine the project launched and failed badly after six months. Work backward to identify the causes and preventive actions.

Why it improves the output

The model makes failure vivid and actionable.

5.20 Game Theory

What it changes

Makes incentives and strategic behavior visible.

Weak prompt

Departments should collaborate more.

Stronger prompt

Use Game Theory to analyse why departments may not collaborate. Identify players, incentives, payoffs, risks, coalitions, and likely strategic moves.

Why it improves the output

The model stops assuming rational goodwill and analyses incentives.

5.21 Information Theory

What it changes

Focuses on uncertainty and signal quality.

Weak prompt

We need better reporting.

Stronger prompt

Analyse our reporting problem using Information Theory. What information is missing, noisy, delayed, compressed, distorted, or misleading?

Why it improves the output

The model focuses on signal quality instead of dashboard quantity.

5.22 Principal-Agent Problem

What it changes

Reveals misaligned incentives.

Weak prompt

The supplier should deliver quality.

Stronger prompt

Analyse this supplier relationship through the Principal-Agent Problem. Where do incentives, information asymmetry, and accountability diverge?

Why it improves the output

The model explains why contractual relationships fail even when everyone appears aligned.

5.23 Pareto Principle

What it changes

Focuses on high-impact minority causes.

Weak prompt

Improve all customer journeys.

Stronger prompt

Use the Pareto Principle to identify the 20% of customer journey steps causing 80% of support tickets, delays, complaints, or lost revenue.

Why it improves the output

The model prioritizes impact instead of completeness.

5.24 Occam’s Razor

What it changes

Reduces unnecessary complexity.

Weak prompt

Build a complex AI governance platform.

Stronger prompt

Use Occam’s Razor to simplify this AI governance proposal. What is the simplest mechanism that adequately manages the real risk?

Why it improves the output

The model avoids over-engineering.

5.25 Hanlon’s Razor

What it changes

Reduces over-attribution of bad intent.

Weak prompt

Teams are blocking the transformation.

Stronger prompt

Use Hanlon’s Razor to reinterpret resistance. Which problems could be explained by confusion, poor incentives, lack of clarity, or bad tooling rather than bad intent?

Why it improves the output

The model becomes less accusatory and more diagnostic.

5.26 Chesterton’s Fence

What it changes

Prevents reckless change.

Weak prompt

Remove this approval step.

Stronger prompt

Use Chesterton’s Fence before removing this approval step. Why might it have been created? What risks might it prevent? What can replace its useful function?

Why it improves the output

The model avoids naive simplification.

5.27 Opportunity Cost

What it changes

Makes trade-offs explicit.

Weak prompt

This project has value.

Stronger prompt

Analyse this project through Opportunity Cost. What other projects, resources, or strategic options will be delayed or sacrificed if we pursue it?

Why it improves the output

The model compares choices instead of evaluating one idea in isolation.

5.28 North Star Metric

What it changes

Aligns activity around core value creation.

Weak prompt

Track signups, usage, NPS, revenue, and clicks.

Stronger prompt

Define a North Star Metric for this product. It must reflect recurring user value, not vanity activity. Explain why other metrics are supporting indicators.

Why it improves the output

The model separates core value from noise.

5.29 Flywheel Logic

What it changes

Looks for compounding loops.

Weak prompt

Publish more content.

Stronger prompt

Use Flywheel Logic to design a growth loop where client cases create proof, proof creates content, content creates leads, and leads create more cases.

Why it improves the output

The model shifts from isolated actions to compounding systems.

5.30 Network Effects

What it changes

Looks at value created by participation.

Weak prompt

Create a community.

Stronger prompt

Analyse this community idea through Network Effects. How does each new participant increase value for others? What contribution loops are needed?

Why it improves the output

The model distinguishes a true network effect from a simple audience.

5.31 Risk Matrix

What it changes

Separates probability and impact.

Weak prompt

AI hallucinations are a risk.

Stronger prompt

Build a Risk Matrix for AI hallucinations by use case. Evaluate probability, impact, detectability, mitigation, and required human review.

Why it improves the output

The model makes risk operational.

5.32 Black Swan Thinking

What it changes

Considers rare but extreme events.

Weak prompt

Plan for normal demand.

Stronger prompt

Use Black Swan Thinking. What rare but extreme events could break this plan? Consider demand spikes, API failure, regulatory shock, reputational crisis, or supplier collapse.

Why it improves the output

The model includes extreme scenarios normally excluded from planning.

5.33 Antifragility

What it changes

Designs systems that improve under stress.

Weak prompt

Avoid all failures.

Stronger prompt

Use Antifragility to design a system that learns from small failures. How can errors improve playbooks, prompts, monitoring, and governance?

Why it improves the output

The model goes beyond resilience and asks how stress can improve the system.

5.34 Minimum Viable Product

What it changes

Reduces initial scope to test learning.

Weak prompt

Build the full platform.

Stronger prompt

Define the MVP. What is the smallest version needed to test the riskiest assumption with real users before building the full platform?

Why it improves the output

The model focuses on learning, not feature volume.

5.35 Double Diamond

What it changes

Separates problem discovery from solution design.

Weak prompt

Jump to the solution.

Stronger prompt

Use the Double Diamond to structure this project: discover the problem, define the core challenge, develop solution options, and deliver a tested version.

Why it improves the output

The model separates exploration and execution.

5.36 5 Whys

What it changes

Pushes toward root causes.

Weak prompt

Projects are late because teams are slow.

Stronger prompt

Use 5 Whys to investigate why projects are late. Continue until you reach a root cause related to governance, decision-making, incentives, or unclear inputs.

Why it improves the output

The model moves beyond symptoms.

5.37 MECE

What it changes

Structures analysis cleanly.

Weak prompt

Analyse problems around strategy, people, tools, process, culture, and planning.

Stronger prompt

Reframe this analysis using MECE categories. Make the categories mutually exclusive and collectively exhaustive, avoiding overlap and gaps.

Why it improves the output

The model produces cleaner structure.

5.38 SWOT

What it changes

Gives a simple strategic diagnostic.

Weak prompt

This market is attractive.

Stronger prompt

Run a SWOT analysis. Separate internal strengths and weaknesses from external opportunities and threats. Keep each point evidence-based.

Why it improves the output

The model organizes strategic context quickly.

5.39 PESTEL / STEEP

What it changes

Expands context beyond the organization.

Weak prompt

The product opportunity looks good.

Stronger prompt

Analyse this product opportunity using PESTEL/STEEP: political, economic, social, technological, environmental, and legal forces.

Why it improves the output

The model broadens the analysis beyond internal assumptions.

5.40 Scenario Planning

What it changes

Avoids betting on one future.

Weak prompt

AI adoption will keep growing.

Stronger prompt

Use Scenario Planning to build four plausible futures for AI adoption. Test which strategy remains robust across all of them.

Why it improves the output

The model treats uncertainty as structural, not accidental.

5.41 Causal Layered Analysis

What it changes

Goes deeper than surface trends.

Weak prompt

People are overwhelmed by technology.

Stronger prompt

Use Causal Layered Analysis: analyse this issue at the levels of litany, systems, worldview, and myth/metaphor.

Why it improves the output

The model moves from symptoms to narratives and worldviews.

5.42 McKinsey 7S

What it changes

Checks organizational alignment.

Weak prompt

We need a new tool.

Stronger prompt

Use McKinsey 7S to test whether this tool will fit the organization: strategy, structure, systems, skills, staff, style, and shared values.

Why it improves the output

The model shows why tools fail when the organization is misaligned.

5.43 RACI

What it changes

Clarifies accountability.

Weak prompt

The team owns the project.

Stronger prompt

Create a RACI for this project. Define who is Responsible, Accountable, Consulted, and Informed for each key decision and deliverable.

Why it improves the output

The model turns vague ownership into operational accountability.

5.44 Cynefin Framework

What it changes

Adapts action to problem type.

Weak prompt

Use best practices for this issue.

Stronger prompt

Use Cynefin to classify this issue as clear, complicated, complex, chaotic, or confused. Recommend the right action mode for each domain.

Why it improves the output

The model avoids applying best practices to complex problems where experimentation is needed.

5.45 PDCA Cycle

What it changes

Creates iterative improvement.

Weak prompt

Roll out the new process.

Stronger prompt

Use PDCA: Plan a pilot, Do the experiment, Check results, Act on what was learned before scaling.

Why it improves the output

The model turns implementation into a learning cycle.

5.46 Bayesian Thinking

What it changes

Updates beliefs with evidence.

Weak prompt

This idea will probably work.

Stronger prompt

Use Bayesian Thinking. Define the initial confidence level, identify evidence that would increase or decrease confidence, and update the recommendation accordingly.

Why it improves the output

The model treats confidence as adjustable, not fixed.

5.47 Expected Value

What it changes

Quantifies uncertain decisions.

Weak prompt

This is risky.

Stronger prompt

Use Expected Value to compare options. Estimate probability-weighted upside, downside, cost, and learning value.

Why it improves the output

The model separates risk from expected return.

5.48 Real Options Thinking

What it changes

Values flexibility under uncertainty.

Weak prompt

Should we build or not build?

Stronger prompt

Use Real Options Thinking. Design a small investment that gives us the right, but not the obligation, to scale later.

Why it improves the output

The model avoids binary decision-making.

5.49 Blindspots

What it changes

Searches for what the analysis may be missing.

Weak prompt

Analyse this strategy.

Stronger prompt

Analyse this strategy and identify blindspots: missing stakeholders, hidden assumptions, ignored risks, absent data, uncomfortable questions, and perspectives not represented.

Why it improves the output

The model is asked to critique its own frame.

5.50 ReAct

What it changes

Combines reasoning and action.

Weak prompt

Research this and give me an answer.

Stronger prompt

Use a ReAct-style approach: reason about what information is needed, take the next useful action, observe the result, then update the answer. Keep final reasoning concise.

Why it improves the output

The model becomes more procedural and evidence-seeking, especially when tools are available.

5.51 Multi-path Selection

What it changes

Compares several solution paths before choosing.

Weak prompt

Give me the best solution.

Stronger prompt

Generate three different solution paths, compare them on cost, speed, risk, reversibility, strategic value, and implementation difficulty, then recommend one.

Why it improves the output

The model avoids prematurely converging on the first plausible answer.

5.52 Ontology Mapping

What it changes

Clarifies entities, relationships, and system structure.

Weak prompt

Organize our business knowledge.

Stronger prompt

Create an ontology map. Identify the core entities, attributes, relationships, hierarchies, dependencies, and rules that structure this domain.

Why it improves the output

The model moves from messy information to a usable conceptual model.

5.53 IFS — Internal Family Systems

What it changes

Maps internal tensions and conflicting “parts.”

Weak prompt

I feel conflicted about this decision.

Stronger prompt

Use an Internal Family Systems-inspired lens. Identify the different internal parts involved, what each part is trying to protect, what each fears, and what a balanced decision could respect.

Why it improves the output

The model can structure inner conflict without reducing it to a simple pro/con list.

5.54 AIDA

What it changes

Structures persuasion.

Weak prompt

Write a sales message.

Stronger prompt

Write the message using AIDA: Attention, Interest, Desire, Action. Make each section explicit and ensure the call to action follows naturally.

Why it improves the output

The model creates a persuasion sequence instead of a flat description.

5.55 Enneagram motivations

What it changes

Adapts messaging to motivational drivers.

Weak prompt

Write a motivating message for this audience.

Stronger prompt

Adapt this message using Enneagram motivations. Address achievement, security, autonomy, harmony, meaning, and enthusiasm without stereotyping individuals.

Why it improves the output

The model considers deeper motivational differences.

5.56 MBTI

What it changes

Adapts communication style to cognitive preferences.

Weak prompt

Explain this strategy to the team.

Stronger prompt

Explain this strategy in a way that speaks to different MBTI-style preferences: big-picture thinkers, detail-oriented planners, people-focused collaborators, and action-oriented implementers.

Why it improves the output

The model creates a more inclusive communication style.

5.57 Hormozi Value Equation

What it changes

Diagnoses which value lever is weak.

Weak prompt

Make this offer more attractive.

Stronger prompt

Analyse this offer using the Hormozi Value Equation: Dream Outcome × Perceived Likelihood divided by Time Delay × Effort/Sacrifice. Identify which lever is weakest and improve it.

Why it improves the output

The model stops vaguely “improving value” and focuses on specific value levers.

5.58 SPIN Selling

What it changes

Moves questioning from features to consequences.

Weak prompt

Prepare sales discovery questions.

Stronger prompt

Prepare discovery questions using SPIN Selling: Situation, Problem, Implication, and Need-payoff. Make the implication questions reveal the cost of inaction.

Why it improves the output

The model creates a stronger consultative sales conversation.

5.59 Gap Selling

What it changes

Anchors value in the distance to be crossed.

Weak prompt

Explain how we improve the process.

Stronger prompt

Use Gap Selling. Define the current state, desired future state, quantified gap, business impact of the gap, and why solving the gap justifies the fee.

Why it improves the output

The model ties the offer to measurable business distance.

5.60 Cialdini’s Influence Principles

What it changes

Adds persuasion leverage to a logical case.

Weak prompt

Make this proposal more convincing.

Stronger prompt

Improve this proposal using Cialdini’s principles: reciprocity, commitment, social proof, authority, liking, scarcity, and unity. Use them ethically and subtly.

Why it improves the output

The model adds persuasion mechanics without relying only on rational argument.

5.61 Dunford Positioning

What it changes

Reframes value against the real alternative.

Weak prompt

Position us as an AI consultancy.

Stronger prompt

Use April Dunford’s positioning logic. Start from the real alternative, identify unique attributes, connect them to value, define the best-fit customer, and choose the strongest market frame.

Why it improves the output

The model positions against what the buyer would otherwise do.

5.62 Challenger / Commercial Insight

What it changes

Shifts from responding to reframing.

Weak prompt

Ask the client what they need.

Stronger prompt

Use a Challenger-style commercial insight. Teach the client something non-obvious about their business, tailor it to their context, and use it to reframe the buying conversation.

Why it improves the output

The model moves from order-taking to insight-led selling.

5.63 StoryBrand

What it changes

Repositions who the hero is.

Weak prompt

Write about our expertise.

Stronger prompt

Rewrite this using StoryBrand. Make the client the hero, us the guide, the problem clear, the plan simple, and the transformation concrete.

Why it improves the output

The model makes the message client-centered.

5.64 Anchoring

What it changes

Sets the buyer’s reference price or frame.

Weak prompt

Present our €8k offer.

Stronger prompt

Use Anchoring to present three pricing tiers high-to-low, so the recommended €8k option is understood relative to a broader value frame.

Why it improves the output

The model understands that perception depends on reference points.

5.65 Decoy Effect

What it changes

Steers tier choice without pressure.

Weak prompt

Create two pricing options.

Stronger prompt

Create three pricing tiers using the Decoy Effect. Make the target tier clearly superior to one nearby option while keeping all offers ethical and legitimate.

Why it improves the output

The model designs choice architecture.

5.66 Loss Aversion

What it changes

Makes cost of inaction visceral.

Weak prompt

Explain the benefits of our solution.

Stronger prompt

Use Loss Aversion to frame the cost of inaction. Show what the client continues to lose each month or quarter if the problem remains unsolved.

Why it improves the output

The model balances upside with avoided loss.

5.67 The Mom Test

What it changes

De-biases discovery.

Weak prompt

Would you buy this product?

Stronger prompt

Rewrite these discovery questions using The Mom Test. Ask about past behavior, real spending, previous attempts, and concrete situations instead of hypothetical interest.

Why it improves the output

The model avoids fake validation.

5.68 BATNA

What it changes

Grounds negotiation in alternatives.

Weak prompt

Help me negotiate the price.

Stronger prompt

Use BATNA. Identify our best alternative, the client’s likely best alternative, the zone of possible agreement, and the walk-away conditions.

Why it improves the output

The model treats negotiation as a comparison of alternatives, not a battle of opinions.

6. Why combining thinking patterns is more powerful than using one

One thinking pattern improves the angle.

Multiple thinking patterns improve the diagnosis.

A single pattern is powerful but partial. Every pattern reveals something and hides something.

For example:

• First Principles reveals fundamentals but may ignore politics.
• JTBD reveals human progress but may ignore system constraints.
• Systems Thinking reveals loops but may become too abstract.
• Theory of Constraints reveals bottlenecks but may underplay culture.
• Goodhart’s Law reveals metric risk but may become overly skeptical.
• MVP reveals testability but may underplay long-term architecture.
• Red Teaming reveals risks but may suppress ambition.

The strongest prompts chain patterns deliberately.

The goal is not to throw ten frameworks into a prompt randomly. The goal is to create a reasoning sequence.

Good chaining usually follows this arc:

1. Clarify the problem
2. Diagnose the system
3. Identify constraints
4. Generate options
5. Stress-test the options
6. Prioritize action
7. Define measurement and learning loops

7. How pattern chaining boosts output quality

Pattern chaining improves LLM outputs in five concrete ways.

7.1 It creates progressive depth

Example chain:

1. First Principles
2. JTBD
3. Systems Thinking
4. Theory of Constraints
5. Leverage Points

This sequence moves from fundamentals to user need, then to system dynamics, then bottlenecks, then action.

The model is less likely to jump to shallow recommendations.

7.2 It creates contradiction and correction

Some patterns balance each other.

Example:

• Occam’s Razor says: simplify.
• Chesterton’s Fence says: do not remove what you do not understand.

Together, they create better reasoning:

“Simplify, but only after understanding what the complexity protects.”

This is stronger than either pattern alone.

7.3 It prevents overfitting to one lens

Every framework can become a trap.

Example:

• SWOT can become generic.
• MVP can become too narrow.
• Risk Matrix can make everything defensive.
• StoryBrand can oversimplify complex B2B buying.
• Systems Thinking can become too abstract.

Combining patterns reduces this risk.

7.4 It improves decision-readiness

A single analysis may be interesting.

A chained analysis can become decision-ready.

Example:

1. PESTEL identifies external pressures.
2. SWOT maps strategic position.
3. Opportunity Cost compares trade-offs.
4. Expected Value ranks options.
5. Real Options defines the first reversible step.

This creates a stronger executive recommendation.

7.5 It makes the model synthesize, not just list

The real power comes from asking the model to resolve tensions between patterns.

Example:

“Where do these lenses agree?”

“Where do they contradict each other?”

“Which recommendation survives all lenses?”

“What should be tested first?”

This forces integration.

8. Prompt combos for high-quality outputs

Below are practical combinations.

8.1 Deep problem diagnosis combo

Use when you need to understand why something is not working.

Patterns

1. First Principles
2. 5 Whys
3. Systems Thinking
4. Theory of Constraints
5. Goodhart’s Law

Prompt

Analyse this problem using:
1. First Principles
2. 5 Whys
3. Systems Thinking
4. Theory of Constraints
5. Goodhart’s Law

For each lens:
- what it reveals
- what it hides
- what assumption it challenges
- what action becomes obvious

Then synthesize:
- the most likely root cause
- the main bottleneck
- the misleading metrics
- the first intervention to test

Why it works

This combo moves from fundamentals to root cause, system behavior, bottleneck, and measurement risk.

8.2 AI adoption strategy combo

Use when building AI adoption in an organization.

Patterns

1. JTBD
2. Systems Thinking
3. Cybernetics
4. Goodhart’s Law
5. Risk Matrix
6. PDCA

Prompt

Design an AI adoption strategy using:
1. Jobs To Be Done
2. Systems Thinking
3. Cybernetics
4. Goodhart’s Law
5. Risk Matrix
6. PDCA

Explain:
- what employees are really hiring AI to do
- what loops reinforce or block adoption
- what feedback signals we need
- which metrics could become misleading
- which use cases need governance
- how to pilot, learn, and scale

Why it works

This combo avoids both extremes: naive experimentation and rigid governance.

8.3 Executive strategy combo

Use for board-level recommendations.

Patterns

1. PESTEL
2. SWOT
3. Wardley Mapping
4. Opportunity Cost
5. Expected Value
6. Real Options

Prompt

Create an executive strategy recommendation using:
1. PESTEL
2. SWOT
3. Wardley Mapping
4. Opportunity Cost
5. Expected Value
6. Real Options

Produce:
- external forces
- internal strengths and weaknesses
- what to build, buy, or partner for
- trade-offs
- probability-weighted options
- a low-regret first move

Why it works

This creates a strategic path without pretending certainty.

8.4 Innovation and product discovery combo

Use when exploring a new product or service.

Patterns

1. Design Thinking
2. JTBD
3. The Mom Test
4. MVP
5. Real Options
6. Bayesian Thinking

Prompt

Explore this product idea using:
1. Design Thinking
2. Jobs To Be Done
3. The Mom Test
4. MVP
5. Real Options
6. Bayesian Thinking

Deliver:
- user context
- target job
- biased questions to avoid
- better discovery questions
- riskiest assumption
- smallest test
- confidence level before and after evidence

Why it works

This combo reduces fantasy and forces market learning.

8.5 Risk and resilience combo

Use when a plan must survive uncertainty.

Patterns

1. Pre-mortem
2. Red Teaming
3. Risk Matrix
4. Black Swan Thinking
5. Antifragility
6. Chesterton’s Fence

Prompt

Stress-test this plan using:
1. Pre-mortem
2. Red Teaming
3. Risk Matrix
4. Black Swan Thinking
5. Antifragility
6. Chesterton’s Fence

For each:
- what could fail
- how severe it would be
- what hidden assumption is exposed
- what safeguard is needed
- what should not be removed too quickly
- how the system can learn from stress

Why it works

This combo identifies both ordinary risks and extreme shocks.

8.6 Sales offer combo

Use when creating or improving a commercial proposal.

Patterns

1. Gap Selling
2. Hormozi Value Equation
3. Dunford Positioning
4. StoryBrand
5. Loss Aversion
6. Cialdini
7. Anchoring

Prompt

Improve this commercial offer using:
1. Gap Selling
2. Hormozi Value Equation
3. Dunford Positioning
4. StoryBrand
5. Loss Aversion
6. Cialdini’s Influence Principles
7. Anchoring

Deliver:
- current state vs desired future state
- quantified gap
- weakest value lever
- positioning against the real alternative
- client-as-hero narrative
- cost of inaction
- ethical persuasion levers
- pricing frame

Why it works

This combo connects business value, positioning, persuasion, and pricing.

8.7 Governance combo

Use when creating policies, decision rights, or operating models.

Patterns

1. Ashby’s Law
2. Good Regulator Theorem
3. RACI
4. Risk Matrix
5. Goodhart’s Law
6. Viable System Model

Prompt

Design a governance model using:
1. Ashby’s Law of Requisite Variety
2. Good Regulator Theorem
3. RACI
4. Risk Matrix
5. Goodhart’s Law
6. Viable System Model

Explain:
- what variety the governance must handle
- what system model is required
- who is accountable for what
- which risks require review
- which metrics could be gamed
- whether the organization has the functions needed to govern this system

Why it works

This avoids both under-governance and bureaucratic overkill.

8.8 Prioritization combo

Use when ranking initiatives.

Patterns

1. Opportunity Cost
2. Pareto Principle
3. Theory of Constraints
4. Expected Value
5. Strategic Alignment
6. Real Options

Prompt

Prioritize these initiatives using:
1. Opportunity Cost
2. Pareto Principle
3. Theory of Constraints
4. Expected Value
5. Strategic Alignment
6. Real Options

For each initiative:
- what it costs us not to do other things
- whether it targets a high-impact minority cause
- whether it addresses the bottleneck
- expected value
- strategic fit
- whether a small reversible test is possible

Why it works

This combo prevents prioritization from becoming political or purely financial.

8.9 Communication strategy combo

Use when crafting persuasive communication.

Patterns

1. StoryBrand
2. AIDA
3. Cialdini
4. Loss Aversion
5. Enneagram
6. MBTI-style communication preferences

Prompt

Create a communication strategy using:
1. StoryBrand
2. AIDA
3. Cialdini’s principles
4. Loss Aversion
5. Enneagram-style motivations
6. MBTI-style communication preferences

Deliver:
- the hero, guide, problem, plan, and transformation
- attention, interest, desire, action
- ethical influence levers
- cost of inaction
- motivational angles
- versions for different communication preferences

Why it works

This combines narrative, persuasion, emotional motivation, and audience adaptation.

8.10 Complex decision combo

Use when the decision is uncertain, political, and strategic.

Patterns

1. Cynefin
2. Game Theory
3. Bayesian Thinking
4. Expected Value
5. Real Options
6. Pre-mortem

Prompt

Help us make this complex decision using:
1. Cynefin
2. Game Theory
3. Bayesian Thinking
4. Expected Value
5. Real Options
6. Pre-mortem

Deliver:
- what type of problem this is
- actors and incentives
- current confidence level
- probability-weighted options
- small reversible bets
- reasons this decision could fail
- final recommendation

Why it works

This combo handles uncertainty without pretending that certainty exists.

9. Recommended master prompt template

Use this when you want a strong multi-lens analysis.

10. Recommended expert-mode prompt

11. Common mistakes when using thinking patterns in prompts

Mistake 1: Using too many patterns at once

Too many patterns can dilute the answer.

Bad:

“Analyse this with 25 frameworks.”

Better:

“Use five lenses, then synthesize.”

Mistake 2: Not asking for synthesis

If you only ask for several patterns, the model may produce separate sections without integration.

Always add:

“Where do the patterns agree and contradict each other?”

Mistake 3: Using patterns as decoration

Bad:

“Use Systems Thinking to make this better.”

Better:

“Use Systems Thinking to identify loops, delays, reinforcing mechanisms, balancing mechanisms, and unintended consequences.”

Name the internal moves of the pattern.

Mistake 4: Asking for conclusions too early

Many people ask:

“What should we do?”

Better:

“First diagnose, then compare options, then recommend.”

Thinking patterns work best in sequence.

Mistake 5: Treating frameworks as truth machines

A framework is a lens, not reality.

Always ask:

“What does this pattern reveal, and what does it hide?”

This makes the model more balanced.

12. The strongest meta-pattern

The most powerful prompt structure is often:

Analyse this topic with:
1. First Principles
2. Jobs To Be Done
3. Systems Thinking
4. Goodhart’s Law
5. Theory of Constraints

For each:
- what it reveals
- what it hides
- how the recommendation changes
- what action becomes obvious

Then synthesize the strongest recommendation.

This works because the five lenses are complementary:

• First Principles strips the problem to fundamentals.
• JTBD reconnects it to human progress.
• Systems Thinking reveals loops and side effects.
• Goodhart’s Law protects against bad metrics.
• Theory of Constraints finds the bottleneck.

Together, they produce a much stronger diagnosis than any single framework.

13. Final takeaway

Thinking patterns are not magic words.

They are cognitive steering devices.

In a prompt, they help the model:

• focus attention
• reduce ambiguity
• activate relevant knowledge
• structure reasoning
• decompose problems
• challenge assumptions
• produce more useful recommendations

Used alone, a thinking pattern improves the angle.

Used in combination, thinking patterns create a reasoning system.

The best prompts do not merely ask the model to produce content.

They design how the model should think before producing it.

That is the shift:

From prompting for answers

to prompting for structured intelligence.

Appendix: Quick pattern selection guide