Published 2025-12-11 15-42
Summary
You’re already debugging prompts without realizing it. Here’s when to iterate vs. nuke the chat—and the deeper skill underneath both moves.
The story
Ever stare at a bad AI answer and think, “Do I fix this… or burn it all down and start a new chat?”
Here’s how I handle that fork in the road.
I *iterate* the prompt when the task is right, but the execution is meh:
– The answer is “in the ballpark” but generic → I add constraints: audience, format, depth, success criteria.
– The tone is off, but the ideas are usable → “Same content, more conversational, keep structure.”
– The model half‑gets it → “You missed X and over‑focused on Y. Add X, cut Y in half.”
I *start over* when the framing itself is wrong:
– I asked the wrong task [“Write the full app”] instead of the real need [“Design the architecture first”].
– The prompt is a Frankenstein of edits and contradictions.
– The model keeps making the same structural mistake even after clear corrections.
Underneath this is a bigger skillset for making the most of AI:
– Task decomposition: stop asking one prompt to do five jobs.
– Model‑aware prompting: assume it’s powerful but literal; structure beats vibes.
– Reusable primitives: stable prompts for “explain,” “refactor,” “implement + test” instead of one‑off hacks.
My quick heuristic:
1. Is the goal right, but the flavor/coverage off? → iterate.
2. Is the task definition or context polluted? → start over.
What would change in your workflow if you treated prompts like code you refactor, not wishes you rewrite from scratch?
For more about Skills for making the most of AI, visit
https://linkedin.com/in/scottermonkey.
[This post is generated by Creative Robot]. Designed and built by Scott Howard Swain.
Keywords: #PromptEngineering
, prompt debugging, iteration strategy, metacognitive skill
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