Eight items in a list became three, and no amount of prompt tweaking brought the other five back.
u/Inevitable-Good219 posted about this on r/PromptEngineering this week, describing months where AI output kept quietly shrinking. Steps went missing. Lists dropped from eight items to three. Nothing was outright wrong, just incomplete.
This is the kind of slow drift that’s easy to miss. Nothing breaks outright, so it doesn’t look like a bug worth reporting. It just quietly costs you a few rounds of “you missed something” every single time.
Quick version if you’re short on time: stop cramming five tasks into one prompt. Send five small messages instead, one task each. The fix isn’t better wording. It’s smaller asks.
Here’s the pattern this Redditor walked through. First move was the obvious one: add “be thorough” and “don’t skip steps” to the prompt. Zero effect. Then came the scolding phase, replying “you skipped step 3, do it again.” That got an apology every time, followed by the same skip on the next run.
Then, on a day this person was rushing, everything changed by accident. Instead of one big prompt asking for a full product page, they split the request into five separate messages, one per item:
- Title
- Description
- Tags
- Pricing note
- Translation
Every output came back complete. No missing steps, no shortened lists!
Old way vs. new way
The old approach: one prompt, five sub-tasks bundled together, hoping the model treats each one with equal weight. The new approach: five prompts, one task each, no bundling at all. Same total request, split into pieces instead of stacked into one message.
This Redditor’s working theory is that the model budgets effort across a task, and a long task means invisible triage on what matters most. They admit they can’t prove it. It could just as easily be that short asks are simply easier to stay on track with. Either theory points to the same fix: stop asking for five things at once.
In their own words, the annoying part was spending months tuning wording when the real fix was chopping the job up.
How to apply this yourself 🛠️
- 🔍 Pull up your last “lazy” output and count how many distinct asks were stacked into that one prompt.
- Split the prompt into one message per task. Title, then description, then tags, then pricing, then translation, each sent on its own.
- Run each small prompt and check it against your usual standard before moving to the next one.
- Compare results. If quality holds across five small prompts where one big prompt failed, you’ve confirmed the pattern for your own workflow.
- 🔁 Keep the split as your default for any multi-part ask going forward, not just as a one-time fix.
A few replies added useful angles. u/Nebty said they prefer starting from the most basic version of a problem and working through it step by step, partly because it surfaces their own hidden assumptions along the way. Someone else asked which model this was tested on, a fair question since results can vary. Another reply joked that the real word for what’s happening is “truncate,” not laziness, which honestly tracks.
The contrast matters more than the specific model or the exact prompt wording. Bundling five tasks into one prompt asks the model to silently prioritize under a single effort budget. Splitting the same five tasks into five messages removes that budget problem entirely, because each message only ever has one job to do.
This isn’t limited to product pages. The same split works for multi-section reports, batch emails, code reviews covering several files, or any task where you’re asking for five things and hoping all five land. Take a weekly report with a summary, three metrics, and a recommendation. Bundled into one ask, the recommendation section is often the first thing to get thin. Split into five sends, each piece gets full attention, and the recommendation stops reading like an afterthought. Anywhere you’re bundling, try unbundling first before you touch the wording again.
If you’ve reworded the same instruction three times this week, that’s the signal to stop. Count the tasks stacked inside that one prompt, split them into separate messages, and see if the next output comes back complete on the first try.
The full thread on r/PromptEngineering has more back and forth on this, including people testing whether the pattern holds across different models. Worth a scroll if you want to see how far others have pushed it.
Frequently Asked Questions
Q: Why does breaking tasks into smaller prompts actually fix the quality issue?
Models seem to budget effort across a task, and large multi-part requests trigger a kind of triage where details get skipped in favor of what feels most important. Smaller, focused prompts keep the model’s full attention on one thing at a time, which cuts down on the “laziness” that shows up on complex tasks.
Q: Does this technique work with all AI models?
The post doesn’t specify which model was tested, but this effort-budgeting behavior is pretty common across different models. It’s worth experimenting with your primary model to see if you get the same improvement, since some handle complexity differently than others.
Q: How do hidden assumptions play into this problem?
When you bundle multiple tasks into one prompt, you’re often skipping steps in your own thinking, betting the model will just “know” what you mean. Breaking things into smaller prompts forces you to spell everything out, which surfaces gaps and ensures nothing gets overlooked.
Q: When should I use separate prompts instead of one big ask?
Use this approach when asking for multiple unrelated deliverables, when outputs feel incomplete, or when one task depends on another finishing first. Simple, single-focus requests (like a single paragraph) usually work fine in one prompt, if you notice quality dropping, that’s your signal to split.
my AI got lazier for months. the fix wasn’t a better prompt.
by u/Inevitable-Good219 in PromptEngineering