Prompt Evolution: SCET (Situation – Complication – Expectation – Task)

AI SCET analytics business sales drop hypothesis clarity

📌 PROMPT EVOLUTION: Exploring AI Frameworks
🎯 Today’s focus — SCET (Situation – Complication – Expectation – Task)

🔹 What is SCET and why use it?

SCET is a go-to framework for business and analytics tasks.
It helps structure your prompt by giving the AI real context, not just vague instructions.

📌 How does it work?

1️⃣ Situation → What’s going on?
2️⃣ Complication → What’s the obstacle or issue?
3️⃣ Expectation → What outcome do you want?
4️⃣ Task → What should AI do?

🔍 Example (before):

Help me understand why my sales are dropping.

💡 The problem:

❌ Too general
❌ No context → AI guesses blindly
❌ Output is a list of random assumptions

🔥 Improved version using SCET — now it’s actionable

✅ Updated prompt:

Situation: The company had stable sales, but they dropped by 30% over the last two months.
Complication: No product changes, the team remained the same, and ad budget hasn’t changed.
Expectation: Understand possible causes and identify directions for recovery.
Task: Provide 5 possible hypotheses + suggest how to test each of them.

📌 What’s better now?

✔ Clear context → AI sees the full picture
✔ Concrete problem → no guessing
✔ Focused expectation and format → analysis and hypothesis
✔ Useful for real business decisions

💡 Bottom line:

Old prompt → AI shoots in the dark
SCET → AI gives structured diagnosis and actionable ideas

🚀 Want your AI to think like an analyst?
That’s part of my AI setup and business automation service — I’ll help you craft prompts that solve real problems with smart output.