Posts tagged Prompt Engineering — AI × software engineering, made practical.
You'll learn to add one "done =" line to your prompts that sets a checkable finish line and fences off the shortcuts you'd reject, so you skip the correction loop.
You'll learn to spot the 'seams' in an AI answer — version, convention, and temporal mismatches the model quietly blends together — and verify those spots first instead of spreading your skepticism evenly.
This lesson explains why generating PR descriptions, specs, and docs with AI costs almost nothing while reading them does not, and why that asymmetry means more generated content lowers the odds any single piece actually gets read.
The score I'd been shipping on for weeks wasn't measuring quality — it was measuring how much my output sounded like the judge itself, and there's a cheap afternoon experiment that exposes it before it costs you more than a prompt.
A team fine-tuned a model to fix a classification bug, celebrated in Slack, and then quietly reverted it — because a rewritten prompt matched it for free and cost nothing to maintain.
A team burned three weeks training a model before an afternoon prompt rewrite did the actual job — here's the order you're supposed to try things in, and the two questions that would've caught it before week one.
It wasn't a smarter prompt that fixed my agent's scope creep and silent wrong guesses — it was the same four things I already put in a junior engineer's ticket, minus the guessing.
A single verb — 'remove' — sat quietly in a ticket for weeks until an agent picked its own meaning, and by the time anyone noticed, the fix wasn't a wording tweak but a whole new way of writing specs.
Your agent isn't running out of room; it's rotting from the inside, and the transcript will lie to your face about it.
The sampler was never the problem — the real source of your 'non-deterministic' agent is hiding in the context window, and most pipelines never check it.
There's a block of text glued to the front of every message you type into an AI, and it explains almost every weird thing that assistant does — once you know to look for it, you can't unsee it.
The obvious fix for a worker agent that keeps breaking its own output format is another agent to catch it — here's why that instinct is usually wrong, and what actually killed the drift for a fraction of the cost.
A one-line prompt edit pasted into Slack took down an unrelated pipeline three days later — and nobody could even say which version of the prompt caused it, let alone fix it.
A planner prompt change that looked like a two-line efficiency tweak quietly broke every downstream executor — and revealed why plans need the same versioning discipline as any API.
Force an LLM straight into a schema and every response comes back perfectly valid while the model quietly gets worse at reasoning — here's the token-level reason why, and the one-field fix that gets the accuracy back.
A senior engineer's battle-tested prompt fell apart the moment a teammate ran the exact same text, and the reason wasn't wording — it was everything he knew that never made it onto the page.
Nothing overflowed, nothing got truncated, and the model still ignored an explicit rule sitting in plain sight — because a bigger context window doesn't buy the model more attention, it buys it more competition for the same fixed budget.
If your first multi-agent run came back with confident garbage, the fix isn't a better model — it's the same delegation skill you already learned the hard way with your first junior hire.
A prompt can run perfectly every single time and still be silently wrong for a week — here's the one question that catches it before your users do.