1. No context
Why it fails: the model guesses your job, audience, and constraints. Guessing multiplies errors.
Quick fix: add role + task scope + constraints.
Before: “Analyze this.”
After: “You are a product analyst. Analyze the attached transcript for founders from seed to Series A to find the vision outliers. Output a 5-bullet decision memo. Max 180 words.”
2. Vague instructions
Why it fails: You didn’t define success.
Quick fix: Define success and acceptance tests.
Before: “Write about marketing trends.”
After: “Write a 1,000-word brief on the three most important B2B AI marketing trends for Q3 2025. Include one data point per trend with a source and a one-line implication.”3. Treating it like Google
Why it fails: Asking questions is level 1. Give it directives.
Quick fix: Change questions into jobs with deliverables.
Before: “What are good onboarding ideas?”
After: “Draft a 5-step onboarding flow for a B2B SaaS. Include the email subjects, timing in days, and one KPI per step.”4. Asking for everything at once
Why it fails: One giant ask hides failures and creates spaghetti outputs.
Quick fix: Split into steps and chain outputs.- Before: “Create our GTM plan, website copy, and investor memo.”
- After, step 1: “List the 5 core customer jobs-to-be-done with a one-line pain for each.”
- After, step 2: “Using the chosen JTBD 2 and 4, write homepage H1 options (5) within 8 words each.”
- After, step 3: “Expand H1 #3 into a 150-word hero section.”
5. Not iterating
Why it fails: It’s a chat. So have a chat with it.
Quick fix: Critique-then-revise loop is the key.
- Before: “Write the article.”
- After, step 1: “List 5 potential angles for my article about [topic] to get eyeballs from [audience]. Focus on [niche].”
- After, step 2: “Using the chosen angle, let’s write 10 SEO-ready titles with their one-liners summaries.”
- After, step 3: “Using the chosen titles and one-liner, let’s create the outline of the article to…”
6. No format or tone
Why it fails: models default to generic structure and bland voice.
Quick fix: force the shape and the voice.
- Before: “Announce the feature.”
- After: “Write a LinkedIn post. 220 words. Hook (2 lines), 3 bullets, one CTA. Tone: direct and practical, no buzzwords, plain English.”
7. No examples
Why it fails: Examples are how models learn your taste.
Quick fix: Add 1–2 gold standards (and optionally one anti-example).- Before: “Write a landing page.”
- After: “Model the tone and density on these two snippets [paste]. Avoid this anti-example [paste]. Keep sentence length under 16 words.”
The R-E-X Prompt
The R-E-X prompt is to define a role, give examples & set expectations in your (ChatGPT, or other AI) prompt.
- Role: Define who the model is and the constraints of the job. Domain. Audience. Risk tolerance.
- Examples: Paste one or two gold outputs to imitate. Add a short note on why they work. Optional anti-example.
- Expectations: State format, length, tone, any banned words, a scoring rubric, and the iteration loop.
R-E-X 3-step checklist
- Write the Role line.
- Paste the Examples.
- Set Expectations: format, word range, tone, rubric, and loop.
Real Scenario Example
- Role: Senior marketing analyst for non-technical leadership. Plain English. No hype.
- Example to imitate: “CTR 1.8% vs 1.2% (+0.6pp). CPC $2.45 vs $3.10 (−21%). So what: shift budget to exact-match.”
- Inputs I’ll paste: timeframe; channels; our metrics; benchmarks (or “none”). If anything’s missing, ask one precise question and stop.
- Task: Compare us vs benchmarks; flag any KPI with ≥15% gap (better or worse).
- Return in order: Summary (≤5 lines + one “so what”); Comparisons by channel inline; Findings (5–7 lines with likely cause + confidence H/M/L); Actions (top 3 with impact %, test days, difficulty L/M/H); Notes (methods + data risks).