Know when to tweak your AI prompts vs starting fresh? I treat prompt engineering like software development – iterate when you’re close, reboot when the foundation is broken.
The story
Ever wonder when to keep tweaking your AI prompts versus scrapping them and starting fresh?
I build prompts like I build software: iterate when I’m getting closer, start over when the foundation is broken.
When do I iterate?
When the output is close. When small tweaks – adding constraints, examples, more context – yield better clarity or precision. When I’m layering instructions for something complex, testing piece by piece.
When do I start over?
When the structure is fundamentally off. When repeated tweaks stop helping. When I’ve misunderstood the problem or overloaded the prompt with too many objectives at once.
After 30 years in software and 8 years working with AI, I’ve learned that prompt engineering is just engineering. The same principles apply.
Skills that make the difference:
Prompt modularization. Break prompts into reusable chunks – context, output format, domain rules. Just like code.
Problem decomposition. Split big challenges into smaller tasks. Single-responsibility, clear interfaces.
Feedback-driven iteration. Review outputs, test alternatives, document what works.
Vibe coding. Tuning tone, formality, or domain language can unlock dramatically better performance.
The trick is knowing when incremental change stops working. If you’re seeing steady progress, keep iterating. If you’re stuck despite your best tweaks, step back and reboot.
Iteration is my first tool. Starting over is my second. Both are essential.
Recent Comments