8/ Final Take 🚀: AI isn’t “set it and forget it.”

It’s a continuous loop: build, test, adjust, monitor, repeat.

I learned this process through many bumps & breakthroughs.

Hope these lessons help you dodge pitfalls (or help me remember 😜). Happy building!

7/ Post-Launch Monitoring 📊: Even if evals are green, real-world use can expose hidden issues.

Track metrics (accuracy, latency, token usage) over time and watch for drift.

Treat your AI like a living thing—it evolves, sometimes even degrades.

6/ Polishing – Make It Smooth ✨: Once it works, level it up.

Improve the UI, optimize token usage, and reduce latency. Add streaming responses if needed.

This is where your feature stops feeling like a hack and starts feeling like a real product.

5/ Re-Architect If Needed 🔄: Sometimes tweaking isn’t enough.

Your one-shot might have been a shortcut for a feature needing multi-step reasoning, or the context window could be too small. If so, rethink your design.

Don’t be afraid to pivot if the architecture is off.

4b/ Pro Tip 💡: If a prompt just won’t work, ask ChatGPT or Cursor Chat “what’s broken”

(but don’t ask it to fix it—it may hallucinate problems and rewrite the whole thing!).

A structured approach beats endless trial-and-error.

4/ Structured Prompt Tuning ⚙️: Once you spot issues, tweak systematically.

Not every failure means you need a prompt change.

Identify specific issues e.g. one-shot not cutting it—and try few-shot instead. Adjust the output format, add a retrieval strategy etc

3b/ Save formal evals until you’re sure it’s worth building on (only the market can tell you that!).

3/ Testing – Your “Vibe Check” 🔍: When you’re still figuring out if the feature has real juice, skip heavy tests.

Do a manual vibe check—try different inputs and see how it responds. If it’s clearly busted, fix it.

2/ Scaffolding POC 🏗️: Kick off by getting something working, even if it’s rough.

Wire your LLM API calls with a placeholder prompt. It’s not about perfection—it’s about testing the idea.

Keep it simple (single-shot calls, no context drama). Overcomplicating wastes time.