ChatGPT Problem-Solving Strategies

Most people don't need more AI tools - They need better prompts: ChatGPT isn't magic, It's a skill. And most people are still using it like a search bar. Here's how to get better results fast: 1. Give it a job ↳Say what role it should play and what you need ↳Ex:"Be a B2B copywriter, write 3 webinar subject lines" 2. Add the goal ↳Say what success looks like, more clicks, clearer writing, faster research ↳Ex:"Make this simple for a busy founder to read fast" 3. Share the audience ↳Good answers depend on who it's for ↳Ex:"Write this for first-time managers at small companies" 4. Give it the raw material ↳Feed it notes, drafts, transcripts, examples, or messy thoughts ↳Ex:"Turn these 6 bullet points into a LinkedIn post" 5. Set the format ↳Ask for the exact shape you want, list, table, email, script, outline ↳Ex:"Turn this into a 5-part outline with short headers" 6. Use constraints ↳Limits improve the output, tone, length, reading level, style ↳Ex:"Keep it under 150 words, simple, direct, no jargon" 7. Ask for options ↳Don't stop at one answer, get versions to compare ↳Ex:"Give me 5 hooks, bold, simple, and curiosity-driven" 8. Make it critique itself ↳Ask it to review the draft and fix weak spots ↳Ex:"What feels vague here, make it clearer and more specific" 9. Fix one thing at a time ↳Change one part at a time, not everything at once ↳Ex:"Keep the message, just make the opening stronger" 10. Show it what good looks like ↳Examples help it match the tone or format you want ↳Ex:"Use this as a style guide, keep the ideas original" 11. Ask better follow-ups ↳The best results usually come in round 2 or 3 ↳Ex:"Make this more practical, add one real example" 12. Use it for thinking, not just writing ↳Use it to plan, sort ideas, find gaps, and test decisions ↳Ex:"Turn this voice note into 3 clear next steps" 13. Build your own repeatable prompts ↳Save what works, the real win is having a reusable system ↳Ex:"Make a prompt template I can use each week" The gap isn't access. It's how you use the tool. Which one of these do most people need to use more? --- ♻️ Repost to help busy professionals get better results from ChatGPT without wasting time. And follow me George Stern for more practical advice.

If you want to get 10X value out of ChatGPT, use what I call the ‘Red Team’ approach to stress test your idea by for flaws. Red Teaming is a term used in cybersecurity for simulating adversity to identify and exploit weaknesses in systems, before an actual threat does. The output is insane and makes some pretty ironclad execution. I use this personally for when I’m about to launch a new product or rip out a sequence or anything related to an idea. What this might look like for you: Prompt: “Run a Red Teaming exercise on my business idea for a fitness app. Your role is to simulate a hostile competitor, investor, or critic. Identify every possible flaw: market risks, feature weaknesses, user behavior issues, retention challenges, scalability problems, and flawed assumptions. Assume your goal is to kill this idea before it launches. Be aggressive, detailed, and relentless.” — Then on the flip side, there’s the blue team approach to DEFEND your idea and to reinforce what red team did. Think of blue teaming as patching the weak points. What this may look like: Prompt: “Now switch to a Blue Teaming perspective. Defend my fitness app idea against the Red Team’s critique. Reinforce the core strengths, address or neutralize the weaknesses they identified, and propose improvements that would make the product more resilient. Justify why this idea should succeed and how it can compete effectively in the current market.” — Now if you want to get even more surgical, apply ‘purple teaming’ as the final layer of feedback. The Purple Team facilitates a feedback loop between both to refine the idea iteratively and intelligently. Identifying which defenses are weak, strong, or need adjustment. Highlighting opportunities created by Red Team findings; e.g., turning a weakness into a differentiator. Example Prompt: “Now run a Purple Teaming exercise on my fitness app idea. Take the Red Team’s critiques and the Blue Team’s defenses, and evaluate the effectiveness of each. Where did the Blue Team successfully mitigate the risks? Where did they fall short? Recommend concrete changes to the product or strategy based on this combined insight. The goal is to strengthen the idea through collaboration between offense and defense.” — Try it for yourself and test the output. The more you know.

STOP asking ChatGPT to "make it better". Here's how to better prompt it instead: ☑ Clearly Identify the Issue Rather than a vague “make it better,” specify the exact element that needs change. For example: "Rewrite the second paragraph so it includes three concrete examples of our product’s benefits. The tone must be formal and persuasive. Remove any informal language or redundant phrases." ☑ Divide the Task into Discrete Steps Break the overall revision into a sequence of manageable tasks. For example: "Go through my instructions, step by step. – Step 1: Summarize it in one sentence. – Step 2: Identify two specific weaknesses. – Step 3: Rewrite the text to address these weaknesses, incorporating specific data or examples." ☑ Specify the Format and Level of Detail Define exactly how the final output should look. For example: "Provide the final revised text as a numbered list where each item contains 2–3 sentences. Each item must include at least one statistical fact or concrete example, and the overall response should not exceed 250 words." ☑ Request a Chain-of-Thought Explanation Ask the model to detail its reasoning process before giving the final output. For example: "Before providing the final revised text, explain your reasoning step-by-step. Identify which parts need improvement and how your changes will enhance clarity and professionalism. Then, present the final revised version." ☑ Conditional Instructions to Enforce Compliance Add if/then conditions to ensure all requirements are met. For example: "If the revised text does not include at least two concrete examples, then add a sentence with a real-world statistic. Otherwise, finalize the response as is." ☑ Consolidate All Instructions into One Prompt Integrate all the detailed instructions into a single, comprehensive prompt. For example: "First, identify the section of the text that needs improvement and explain why it is lacking. Next, summarize the current text in one sentence and list two specific weaknesses. Then, rewrite the text to address these weaknesses, ensuring the revised version includes three concrete examples, uses a formal and persuasive tone, and is structured as a numbered list with each item containing 2–3 sentences. Each list item must include at least one statistical fact or example, and the overall response must be no longer than 250 words. Before providing the final text, explain your reasoning step-by-step. If the revised text does not include at least two concrete examples, add an additional sentence with a real-world statistic." ___ Why This Works People never give enough context. And once ChatGPT answers, they never correct it enough. Think about it like an intern. Deep prompting is all about precision: give clear instructions, context & the right corrections. PS: Don't forget to use the new o3-mini model. It's crushing any other one. Yes – even DeepSeek.

I've been testing ChatGPT Pro ($200/month), and here are a few use cases where I think it’s worth the price: Deep Researcher – By far the best research tool I’ve used, outperforming anything else available. o1 for complex prompt engineering – Handles high-context, intricate problems quite well. Some tasks that exceeded my expectations: - User feedback analysis: Study all app reviews for [product] related to [specific user journey], then generate recommendations aligned with [best practices 1, 2, 3]. - 360° Brand Positioning Study: Analyze [product] from three perspectives—how users perceive us online, how clients see us, and how government bodies view us. - Market Trends Research: Review all startups in [vertical] backed by [accelerators 1, 2, 3] and/or [VCs 1, 2, 3], then surface emerging trends. - Competitive Analysis: Map out competitors based on [company type] across [countries XYZ], segmenting them into startups and larger companies. - GPT-4.5 Prompting: "You are an assistant that engages in extremely thorough, self-questioning reasoning. Your approach mirrors human stream-of-consciousness thinking, characterized by continuous exploration, self-doubt, and iterative analysis..." Lastly, a few techniques that have worked well for me: - Few-shot prompting – providing clear examples of what I need upfront. - Asking the model to confirm its understanding before it starts, often going back and forth until I’m convinced it fully grasps my needs. - Always use code for math-related tasks. - Chain of Thought (CoT) reasoning – Either using CoT-enabled models (o1, o3-mini, o3-mini-high) or prompting simpler models to break down problems into structured, step-by-step reasoning. -  Asking the model to reassess its own recommendations and iterate with constructive feedback until it no longer provides meaningful improvements. - Encouraging the model to explore various prompt engineering approaches to determine the most effective one for my specific task. I’d love to hear—any prompt techniques that have worked particularly well for you?

Large Language Models (LLMs) like ChatGPT have showcased their prowess and versatility across various industries, despite being introduced to the public just a year ago. This blog, authored by the Engineering team at Oscar Health, details their use of ChatGPT 4 in developing an insurance claim assistant function. This assistant is designed to answer customer queries about their claims effectively. In tackling this project, the team employed several unique strategies and solutions. Firstly, they translated complete claim information into a domain-specific language termed “Claim Trace,” enabling ChatGPT to convert structured data into natural language. To enhance the model's performance, they implemented a method akin to providing a table of contents, which aids ChatGPT in better understanding the structure of Claim Trace. Another strategy involved a chain-of-thought approach with function calling, directing ChatGPT to break down a complex problem into smaller, more manageable segments. Additionally, they incorporated an iterative retrieval function, prompting ChatGPT to seek further information in cases of high uncertainty, thereby ensuring more accurate responses. These three methodologies combined to yield great results. The team reported a 100% accuracy rate in simpler cases and over 80% accuracy in more complex scenarios. This achievement boosted the company's operational efficiency and demonstrated how to fine-tune LLMs like ChatGPT to effectively meet specific business objectives. – – –  Check out the "Snacks Weekly on Data Science" podcast and subscribe, where I explain in more detail the concepts discussed in this and future posts:    -- Apple Podcast: https://lnkd.in/gj6aPBBY   -- Spotify: https://lnkd.in/gKgaMvbh #datascience #chatgpt #llm #finetuning #largelanguagemodels #engineering #healthcare https://lnkd.in/gRnf_KmV

𝗦𝘁𝗼𝗽 𝗮𝘀𝗸𝗶𝗻𝗴 𝗔𝗜 "𝗛𝗼𝘄 𝗰𝗮𝗻 𝘆𝗼𝘂 𝗵𝗲𝗹𝗽 𝗺𝗲?" 𝗦𝘁𝗮𝗿𝘁 𝗮𝘀𝗸𝗶𝗻𝗴 "𝗪𝗵𝗮𝘁 𝘀𝗽𝗲𝗰𝗶𝗳𝗶𝗰 𝗽𝗿𝗼𝗯𝗹𝗲𝗺 𝗮𝗺 𝗜 𝘀𝗼𝗹𝘃𝗶𝗻𝗴?" Most people open ChatGPT and type vague requests like "help me with marketing" or "give me business ideas."  Then they wonder why the responses feel generic. The issue isn't the AI. It's your question. Problem definition beats prompt engineering every time. Instead of: "Help me grow my business" Try this: "My sales team is missing 30% of quarterly targets. Deals slowed from 60 to 90 days. Each missed quarter costs $2M in projected revenue." Now AI can actually help you. With a clear problem, you can ask targeted questions:  • Analyze patterns in top-performing deals  • Research what drives faster sales cycles in your industry • Generate hypotheses about pipeline bottlenecks 𝗧𝗵𝗲 𝗳𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸 𝗶𝘀 𝘀𝗶𝗺𝗽𝗹𝗲: 1. Define the specific problem and its business impact 2. Quantify what success looks like 3. Use AI to research and validate solutions Six months of applying this approach will transform how you work.  Not because you become an AI expert, but because you master problem definition. The best AI users aren't prompt engineers. They're problem definers. 𝗥𝗲𝗮𝗱 𝘁𝗵𝗲 𝗳𝘂𝗹𝗹 𝗻𝗲𝘄𝘀𝗹𝗲𝘁𝘁𝗲𝗿 𝗵𝗲𝗿𝗲: https://lnkd.in/eHDpy-fn Found this helpful?  𝗟𝗶𝗸𝗲 𝗮𝗻𝗱 𝗿𝗲𝗽𝗼𝘀𝘁 to share with your network. 𝗙𝗼𝗹𝗹𝗼𝘄 𝗺𝗲 for more insights on using AI strategically in business. Got a specific problem you're trying to solve? 𝗗𝗠 𝗺𝗲 - I'd love to hear about it.

Stop asking ChatGPT to "Research this topic." That's why you get Wikipedia-level answers. And why the insights never go deep enough to be useful. If you want research that actually informs your decisions and gives you a real edge, you need to tell it exactly what kind of intelligence you're after. Use these prompts instead: 1. The Deep Dive Research "Act as a subject matter expert with 20 years of experience in [topic/industry]. Research this topic for me: [topic]. Go beyond surface-level facts. Uncover the nuances, conflicting perspectives, emerging developments, and what most people get wrong about this subject. Present your findings in a structured way that helps me truly understand the landscape." 2. The Competitive Intelligence Research "Act as a competitive intelligence analyst. Research [company/industry/competitor] and give me a comprehensive breakdown of their strengths, weaknesses, market positioning, and strategy. Identify gaps they're not addressing, opportunities they're missing, and what I can learn from both their successes and failures." 3. The Trend Forecasting Research "Act as an industry futurist and research the current trends shaping [industry/topic]. Identify what's growing, what's declining, and what's emerging on the horizon that most people haven't noticed yet. For each trend, explain the driving forces behind it, who it impacts most, and what opportunities or threats it creates in the next 1 to 3 years." 4. The Problem-Specific Research "I'm trying to solve this specific problem: [describe problem]. Research everything relevant to solving it including root causes, proven solutions, failed approaches, expert recommendations, and real-world case studies. Prioritize insights that are practical and immediately applicable to my situation: [add context about your situation]." 5. The Audience Research "Act as a consumer psychologist and research this target audience for me: [describe audience]. I need to understand their deepest frustrations, unspoken desires, decision-making triggers, common objections, and what language they use to describe their own problems. Use this to help me communicate with them more effectively and build something they actually want." 6. The Strategic Research Brief "Act as a senior research analyst. I need a strategic research brief on [topic] for this specific purpose: [explain your goal, decision, or project]. Structure your findings around what I actually need to know to move forward confidently. Include key facts, critical insights, potential risks, and a clear summary of what the research means for my specific situation." P.S. ~ For more updates like this: 1. Scroll to the top 2. Click "View my newsletter" 3. Subscribe, and you'll never miss a thing in the world of AI ever again.

Most people prompt every AI the same way. That’s why their outputs are mediocre. I’ve tested hundreds of prompts across every major AI platform. The difference between average and exceptional outputs isn’t prompt length. It’s prompt style matched to the tool. This framework breaks it down: ChatGPT → Prompt like an instructor. Start with a role assignment: “Act as a productivity coach.” Define the specific task. Ask for step-by-step action plans with timelines. Specify your desired format—table, outline, bullet list. Request tool recommendations. ChatGPT excels at structured guidance and task planning. Give it constraints and it delivers. Perplexity → Prompt like a research analyst. Lead with specific information requests. Include relevant keywords, timeframes, and geographies. Ask for cited sources and reference links for verification. Request trend summaries with citations. Follow up with comparison questions that require data-backed reasoning. Perplexity is built for evidence-based analysis. Treat it like a junior analyst who needs clear research parameters. Grok → Prompt like a candid friend. Use conversational tone: “Hey Grok, what do you think about…” Add emotional context. Ask for honest, unfiltered feedback and alternative perspectives. Request comparisons or opposing viewpoints to challenge your assumptions. Ask for common pitfalls and mistakes to avoid. Grok thrives on casual brainstorming and identifying blind spots others miss. Gemini → Prompt like a project planner. Explain the overall project goal upfront. Define expected outputs—tasks, subtasks, timelines. Ask about Google Workspace integrations. Request detailed weekly or daily action plans. Ask for dependency breakdowns and milestones. Request formatted outputs like tables and charts. Gemini is optimized for project management and collaborative workflows. Why this matters: Each model has a personality bias baked into its training data and architecture. ChatGPT leans toward structured helpfulness. Perplexity toward verification and sourcing. Grok toward irreverence and contrarianism. Gemini toward organizational workflows. When you fight these tendencies, you get generic outputs. When you lean into them, you unlock capabilities most users never see. The tactical shift: Stop copying prompts between platforms. Start adapting your communication style to each tool’s strengths. Same question, different framing = dramatically different quality. One prompt style for all tools is lazy. Adapted prompting is leverage.

When AI meets the Theory of Constraints — the result is next-level clarity. My friend Dan Martell, one of the sharpest minds I know in SaaS and Business sScaling, recently shared how he uses ChatGPT Voice Mode — not just as a productivity tool, but as a thinking partner to decide what the ONE thing is to focus on next. Dan and I share a deep appreciation for the Theory of Constraints (TOC)... In his story, Dan goes for a run, talking to ChatGPT about a tough strategic decision. By the end of the 45-minute run, AI didn’t just give him advice — it cut through the noise and showed him exactly what to do next. What made it powerful wasn’t the AI itself — it was how he used it. He gave it context and constraints — the situation he was in, what he’d tried, and his limitations — and told it to challenge him (using the TOC framework) to find the ONE thing he can and should do next AI without context, constraints, and a focusing framework like TOC generates noise. But with them, it generates signal — the ONE thing we need to succeed. Context can means telling AI your ONE Goal and what hasn’t worked. Constraints mean real-world boundaries, like: “I need something I can apply this week, at no cost, to generate more revenue.” That’s exactly what I suggested to a client who felt overwhelmed and wanted to use ChatGPT: His Q: “I am feeling overwhelmed with all the fires I'm fighting” I gave it his context and my framework — the ONE Thing Focusing Cycle (OTFC) — which guides ChatGPT to think through One Goal, One Constraint, One Problem, One Conflict, One Innovation, and One Experiment at a time. Here’s how ChatGPT helped him apply it 👇 🌀 Applying the ONE Thing Focusing Cycle ONE Goal:  “Free up my time to focus on strategic growth.” ONE Constraint:  “I have limited attention and budget” ONE Problem:  “I’m reacting to what’s urgent instead of what’s important.” ONE Conflict: “Start Focusing only on what’s important to grow, but then urgent issues might slip and create fires. Continue been distracted by urgent tasks to stay reliable, but important goals get delayed.” Each side has pros and cons — growth vs. stability — which is why the problem persists. ONE Innovation: “Block 2 hours each morning for high-leverage work before opening email — protecting time for strategy without neglecting operations.” This resolves the trade-off — keeping pros of both without cons of either. ONE Experiment:  “Try it for one week and measure progress.” By adding context, constraints, and the right framework, AI becomes a thinking partner that sharpens focus and confidence. As Dan said: “Using AI this way — not just for answers, but for thinking better — will make you a ton of money and a ton more impact.” I couldn’t agree more. Because the future of leadership isn’t about knowing more — it’s about focusing better. Question for you: What context, constraints, or frameworks — like the TOC — are you including in your prompts when asking ChatGPT for help?

Most writers use ChatGPT like a basic thesaurus. They're missing 90% of its language intelligence. You type "outrage synonym." Get a list of words. Pick one that sounds fancy. Your sentence now reads weird. Professional writers use ChatGPT completely differently. They treat it like a linguistics professor who explains connotation. The 4-step workflow that eliminates awkward word choices: 1. Provide full context, not just the word Don't ask for synonyms in isolation. Paste your entire sentence. Show where the word appears. "For months, the mayor ignored public outrage over the polluted lake." Context changes which replacement actually works. 2. Specify the creative direction Tell ChatGPT what you're actually trying to say. "I want something more metaphorical." Or: "I need a phrase about consequences catching up." The AI suggests phrases, not just word swaps. You get "chickens coming home to roost" instead of "recompense." 3. Get categorized options by tone Ask for alternatives to a single word. ChatGPT returns suggestions grouped by intent. More Dramatic: "fury" More Colloquial: "anger" More Formal: "indignation" You pick based on your audience, not random preference. 4. Test if the word actually fits Before committing, ask ChatGPT to validate. "Does this work? For months the mayor ignored public grief over the polluted lake." The AI explains why "grief" implies personal loss. While "outrage" implies moral anger. You learn the subtle difference you'd miss otherwise. Traditional thesaurus gives you synonyms without teaching you when to use them. ChatGPT explains connotation in real-time. You become a more precise writer, not just someone who swaps words randomly. Most people think fancy vocabulary makes better writing. When precision in word choice is what actually matters. Found this helpful? Follow Martin Crowley.