AI prompting isn’t magic — it’s engineering. After countless experiments, researchers and builders have learned what actually works. Here are the key takeaways 👇
1. In-Context Learning Wins
Forget long instructions. Give the model a few solid examples — it learns faster and performs better.
2. Treat Prompts Like Code
One word can break everything. Version-control your prompts, run tests, and track performance.
3. Test, Don’t Guess
Your “perfect” prompt might fail in real cases. Build a small test suite with tricky examples to check reliability.
4. Domain Experts > Prompt Engineers
If it’s medical AI, let doctors write prompts. Real expertise beats clever wording.
5. Tune Temperature
Sometimes changing it from 0.7 to 0.3 is all it takes to fix inconsistency. Simple but powerful.
6. One Model ≠ Another
What works for GPT-4o might confuse Claude or Llama. Optimize per model — not one-size-fits-all.
7. Simplicity Beats Complexity
Long chain-of-thought prompts aren’t always better. Start simple, and only add reasoning when it helps.
8. Let AI Help You Prompt
Yes, use AI to write better prompts for AI. It’s meta — and surprisingly effective.
9. System Prompts Are Everything
Most problems come from weak system instructions. Fix your foundation first.
Bonus: Always test for prompt injection attacks early — one bad input can crash your whole system.
A wise approach, simple is sophisticated. Thanks for your care