--- title: Problem Solving Pattern type: article created: '2026-04-05' updated: '2026-04-05' source_docs: - raw/2025-11-13-using-ai-part-2-101398141.md tags: - ai - prompting - problem-solving - brainstorming - verbalized-sampling - probability layer: 2 client_source: null industry_context: null transferable: true --- # Problem Solving Pattern A structured AI prompting approach for generating **distinct solution perspectives** across multiple disciplines. Rather than asking for a single answer or receiving minor variations of the same idea, this pattern forces the AI to sample broadly and surface unconventional approaches — including technical, financial, human-centric, strategic, and asymmetric angles. Related: [[wiki/knowledge/ai-tools/verbalized-sampling]] · [[wiki/knowledge/ai-tools/probability-control]] --- ## The Problem This Solves A simple prompt like "how do I fix my stagnating business?" tends to produce the most probable, most generic answer — the same answer every other user is getting. This is what Mark Hope calls **"AI slop"**: output that is technically correct but competitively useless because it's commoditized by default. The Problem Solving Pattern breaks this by explicitly demanding variety, multi-disciplinary framing, and probability scoring in a single prompt. --- ## Core Prompt Structure ``` I'm facing this problem: [describe the specific situation]. I need five different ways to approach or solve this. Please generate five distinct solution perspectives, each from a different angle or discipline. For each solution: - Name the angle or discipline - Describe the core approach - Assign a probability score (0–1) showing how obvious or unexpected this solution is ``` **Key constraints to include:** - `five distinct` — triggers [[wiki/knowledge/ai-tools/verbalized-sampling]], preventing minor variations - `each from a different angle or discipline` — forces multi-disciplinary spread - `assign a probability` — enables [[wiki/knowledge/ai-tools/probability-control]] so you can identify and discard obvious answers --- ## Example: Flynn Audio (Stagnating Automotive Stereo Business) Prompt used in the session: > *"My automotive stereo sound and safety equipment business is stagnating. I need five different ways to approach or solve this. Please generate five distinct solution perspectives, each from a different angle or discipline. For each solution, assign a probability to show how obvious or unexpected it is."* **Results:** | Angle | Core Idea | Probability | |---|---|---| | Technical Innovation | Pivot to AI-powered, OTA-updating audio systems — "the Tesla of aftermarket" | Low | | Financial Engineering | Shift from one-time sales to Equipment-as-a-Service recurring revenue | Moderate | | Human-Centric Design | Stop selling equipment; sell "automotive sanctuaries" — sound sommelier consultations, stress-reduction acoustic packages | Moderate-Low | | Strategic Repositioning | Exit saturated consumer market; become exclusive supplier to autonomous vehicle fleets and ridesharing services | Low | | Unconventional / Asymmetric | Become a Trojan Horse Data Company — installed equipment as IoT sensors gathering anonymous driving and acoustic data | Very Low | The final option (Trojan Horse Data Company) is the kind of idea that would never surface from a simple prompt. It emerged because the pattern explicitly demanded an "unconventional" discipline. --- ## Discipline Axes to Prompt For When you want to ensure genuine variety, you can name the disciplines explicitly or let the AI choose. Common productive axes include: - **Technical / Product** — engineering, R&D, software - **Financial / Business Model** — pricing, revenue structure, capital - **Human-Centric / Design** — psychology, UX, experience design - **Strategic / Market** — positioning, segmentation, competitive dynamics - **Unconventional / Asymmetric** — cross-industry analogies, contrarian moves, long-tail ideas > ⚠️ Mark Hope notes: *"You don't have to give these examples if you don't want, because they can be leading — it may give you exactly what you say here. Sometimes it's safer to say just 'each from a different angle.'"* --- ## Extending the Pattern: Tail Sampling After the initial five solutions, you can push further into unconventional territory: ``` Now generate five more solutions, sampling from the tail of the distribution — prioritizing responses with probabilities below 0.1. ``` This is most useful when: - The client's market is commoditized and conventional differentiation won't work - You need to stimulate creative thinking, not find an immediately deployable answer - You're preparing for a client brainstorm and want provocative starting points See [[wiki/knowledge/ai-tools/probability-control]] for full tail-sampling guidance. --- ## When to Use This Pattern | Situation | Use This Pattern? | |---|---| | Client business is stagnating or commoditized | ✅ Yes | | Need to present multiple strategic options | ✅ Yes | | Looking for a single definitive answer | ❌ No — use a direct prompt | | Generating ad copy or creative assets | ⚠️ Partial — see [[wiki/knowledge/ai-tools/ad-copy-pattern]] | | Predicting future market scenarios | ⚠️ Partial — combine with scenario framing | --- ## Important Caveats - **Verify before presenting.** AI may surface ideas that already exist verbatim. Michał Bielerzewski checked six slogans from the session and found three already in use. Always search before presenting to a client. - **AI as stimulus, not authority.** The goal is to stimulate human thinking and open up the solution space — not to hand the output directly to a client. Use these ideas as a starting point. - **Model choice matters.** Claude tends to produce more genuinely unconventional output than ChatGPT for this pattern. ChatGPT skews toward the center of the probability distribution. Grok and Gemini behave differently again. Test across models for important problems. --- ## Related Patterns - [[wiki/knowledge/ai-tools/verbalized-sampling]] — the "give me five distinct" technique - [[wiki/knowledge/ai-tools/probability-control]] — requesting and interpreting probability scores - [[wiki/knowledge/ai-tools/rapid-brainstorming-pattern]] — lighter-weight idea generation without discipline framing --- ## Source Demonstrated by Mark Hope in the internal AI training session *"Using AI Part 2: Verbalized Sampling and Probability Control"* (2025-11-13). Prompt templates from this session were distributed to the team as a follow-up document.