This is huge validation for what we're building at TestFeed. PyMC Labs + Colgate just published research showing LLM-based personas can predict purchase intent at 90% of human test-retest reliability across 57 real consumer studies. Not 90% accuracy - 90% of the maximum

CHATGPT-5.2 JUST TURNED LEARNING INTO A PAYWALL-FREE People are still buying courses, sitting through playlists, and bookmarking “learn later” links while ChatGPT-5.2 can design a personalized curriculum, teach you in real time, test your understanding, and adapt on the fly for

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AI makes content creation faster than ever, but it also makes guessing riskier than ever. If you want to know what your audience will react to before you post, TestFeed gives you instant feedback from AI personas that think like your real users. It’s the missing step between

5/ THE PROJECT MODE Skills don’t stick unless you build. Prompt to steal: “Give me 5 real world projects to practice this. Then guide me step by step through the first one.” You’ll learn faster than any passive course.

4/ THE PERSONAL TUTOR MODE This is where it gets insane. Prompt to steal: “Ask me 10 questions to diagnose what I don’t understand about [topic]. Then reteach the weak spots with examples.” This feels like hiring a $200 per hour tutor.

3/ THE RAPID SKILL DOWNLOAD Want mastery without fluff? Tell ChatGPT to cut the nonsense. Prompt to steal: “Give me the 20 percent of [topic] that drives 80 percent of results. Be brutally practical.” This is the Pareto move.

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2/ THE “TEACH ME LIKE I’M LAZY” METHOD If the roadmap feels heavy, compress it. Prompt to steal: “Summarize this topic in 10 key principles. Then give me examples so I never forget the concepts.” This is the cheatcode for retention.

1/ BUILD YOUR “AI DEGREE” IN 30 SECONDS Pros don’t ask “teach me X”. They ask for the full roadmap. Prompt to steal: “Create a complete learning curriculum for [skill]. Break it into beginner, intermediate, and advanced modules. Add exercises, real world projects, weekly

CHATGPT-5.2 JUST TURNED LEARNING INTO A PAYWALL-FREE People are still buying courses, sitting through playlists, and bookmarking “learn later” links while ChatGPT-5.2 can design a personalized curriculum, teach you in real time, test your understanding, and adapt on the fly for

AI makes content creation faster than ever, but it also makes guessing riskier than ever. If you want to know what your audience will react to before you post, TestFeed gives you instant feedback from AI personas that think like your real users. It’s the missing step between

Pick ONE technique from this thread. Use it in your next 10 prompts. Document what changes. Most people will read this and do nothing. Engineers will test it tomorrow and 10x their results by next week. The techniques are free. The discipline to use them isn't.

Every one of these techniques does the same thing: reduces ambiguity. The model isn't "thinking" less. It's thinking in a more constrained space where wrong answers are structurally impossible. You're not making it smarter. You're making it harder to be wrong.

Here's why these work: The pattern: these techniques all add structure AROUND the generation step. Bad prompting: "Do the thing" Engineer prompting: "Plan how you'll do the thing, do the thing, verify the thing" Models are prediction engines. Give them a better scaffold and

Technique 6: Chain-of-Verification The model generates an answer, then immediately verifies it against stated requirements. Self-correction catches 60%+ of errors that would slip through. Template: [Your request] After generating, verify your output against these criteria: 1.

Technique 5: Specification-Driven Generation Engineers write a spec first, get model agreement, THEN generate. This separates "what to build" from "how to build it" and catches misalignment early. Template: First, write a specification for [task] including: - Inputs and their

Technique 4: Differential Prompting Engineers don't ask for one output. They ask for two versions optimized for different criteria, then pick or merge. This exploits the model's ability to hold multiple solution strategies. Template: Generate two versions of [output]: Version

Technique 3: Metacognitive Scaffolding Instead of asking for an answer, engineers ask the model to explain its reasoning process BEFORE generating. This catches logical errors at the planning stage. Template: Before you [generate output], first: 1. List 3 assumptions you're

Technique 2: Multi-Shot with Failure Cases Everyone uses examples. Engineers show the model what NOT to do. This creates boundaries that few-shot alone can't establish. Template: Task: [what you want] Good example: [correct output] Bad example: [incorrect output] Reason it

Technique 1: Constraint-Based Prompting Most prompts are too open-ended. Engineers add hard constraints that force the model into a narrower solution space, eliminating 80% of bad outputs before they happen. Template: Generate [output] with these non-negotiable constraints: -

OPENAI, ANTHROPIC, AND GOOGLE KNOW SOMETHING about prompting that most creators don’t. Steal these 6 techniques and your outputs won’t look human anymore 👇

AI makes content creation faster than ever, but it also makes guessing riskier than ever. If you want to know what your audience will react to before you post, TestFeed gives you instant feedback from AI personas that think like your real users. It’s the missing step between