90% of founders waste years building products nobody wants. They all skip the ONE question that matters most. Answering it is how I got strangers on Reddit to pay me for an 'AI' startup before even having a product. The "reverse launch" method—get paid first, build second: 🧵

Bookmark this post so you can revisit in the future. If this framework helped you, follow @thedaveclark for more AI automation and startup insights. I share strategies from building with AI at Amazon and my VC-backed startup. RT the first tweet to help others master AI.

Generate videos in just a few seconds. Try Grok Imagine, free for a limited time.

The counterintuitive truth:. Powerful AI isn't about complex prompts. It's about simple, precise instructions. Master this, and you'll be in the top 1% of AI builders.

Quick implementation summary:. 1. Break your large task into micro-prompts.2. Write PACT (Persona, Action, Context, Template) prompts for each decision.3. Be painfully explicit.4. Write in the affirmative.5. Eliminate logical conflicts.6. Turn down the temperature (usually).7.

Framework Rule #8: Freeze & Test. Most models auto-update by default:. - Freeze your model version.- Build regression tests.- Test EVERY prompt change. A "minor" version release can break your workflow. Treat prompts like production code. Source control + unit tests on changes.

Framework Rule #7: Output Constraints. Force structure with:. - JSON schemas.- Numbered lists.- Character limits.- Required fields. Constraints paradoxically improve quality. The LLM knows exactly what success looks like.

Framework Rule #6: Turn Down the Temperature. Temperature controls randomness. For consistency:.- Business logic / Critical decisions: 0-0.2.- Some variation (email templates, product descriptions): 0.3-0.6.- Creative tasks: 0.7+. Lower temp = more predictable outputs.

Framework Rule #5: Eliminate Logical Conflicts. Your prompt can't contradict itself. Example:.❌ "Be extremely detailed but keep it under 50 words".❌ "Use formal language but sound casual and friendly". ✅ Fix: "Use 50 words. Include price, timeline, and next steps.".

Framework Rule #4: Write in the Affirmative. Tell the LLM what TO do, not what NOT to do. LLMs follow instructions better than restrictions. ❌ "Don't be verbose or use technical jargon".✅ "Use simple words. Write sentences under 15 words.". Positive instructions = consistency.

Framework Rule #3: Be Painfully Explicit. LLMs don't infer. They predict. Every ambiguity = inconsistency. Example:.❌ "Summarize this briefly".✅ "Summarize in exactly 3 bullet points, each 15 words maximum".

Framework Rule #2: Make a PACT. P - Persona: Give your LLM a specific role.A - Action: Define ONE clear action.C - Context: Provide relevant background.T - Template: Structure the output format. This primes the model's internal weights to better complete the task at hand. P -.

Breaking large task into micro-tasks = cumulatively higher accuracy. At the end of the day LLMs are trying to predict the next token. The more a prompt tries to do, the more likely it is to fail. The goal is consistent behavior from your LLM

Framework Rule #1: Ask Less of Your LLM. ❌ Bad: "Take this customer support ticket, classify, prioritize, route, and write a response". ✅ Good: Break prompt into micro-tasks:. Prompt 1: Classify the ticket type.Prompt 2: Triage.Prompt 3: Route to team.Prompt 4: Draft response.

Complex prompts = inconsistent results. Simple prompts = reliable outputs. Think of it this way:. ❌ "Handle this appropriately". ✅ "If angry customer, apologize. If confused, clarify. If happy, thank.". The secret? Break everything down AND add examples.

Additionally - Einstein's rule for physics applies almost perfectly to AI prompts:. "Everything should be made as simple as possible, but not simpler.". Strip away complexity, but keep what's essential. That's the entire framework.

First, the counterintuitive truth:. Prompt failures usually come from putting TOO MUCH in your prompt. Like business writing - the best memos are written at a 5th-grade level. Clear. Simple. Zero ambiguity.

Most AI prompts break in production. Not because they're bad prompts. Because they ask too much. After 1000+ hours building AI systems, here's what actually works (& what Einstein had to say about prompting LLMs)

The 80/20 rule of AI programming:. AI handles 80% of tasks brilliantly (hello world, simple scripts, boilerplate). The other 20% (complex business logic, production debugging, system architecture) requires deep expertise AI doesn't have. That 20% is where the money is.

The "demo effect" in AI programming:. Perfect demonstrations in controlled environments create false confidence about real-world capability. It's like assuming someone who can cook a perfect dish following a recipe can run a restaurant kitchen during dinner rush. Similar.

Figma's IPO today was a short-term win but a long-term mistake for Wall Street. Watching $3B of market value flow straight to middlemen just inspired tech’s next wave of direct listings.

Watching non-programmers declare "coding is dead" reminds me of watching people who can't drive declare that self-driving cars have solved transportation. They can't distinguish between demo conditions and real-world complexity because they've never navigated the real world.