u/Bellleq

Most AI productivity advice is useless. Vague stuff about "prompt engineering" that sounds smart but changes nothing.

I started using AI for work about a year ago and spent the first few months doing it completely wrong. Was copying the same context into every chat, rewriting instructions from scratch each time, treating it like a fancy search engine.

I sat at my kitchen table one Tuesday night realizing I'd spent about 40 minutes setting up a conversation I already had three times that week. That was when it clicked. Save your context once, stop repeating yourself. Sounds obvious but I genuinely didn't get it for months.

The other thing nobody mentions is matching different models to different tasks. I used to throw everything at the most powerful option. Drafting emails, cleaning up notes, summarizing recordings from meetings. The smaller faster ones handle about 80 percent of that just fine, and you stop burning through limits by 3pm. Voice input changed how I process stuff too, I talk through decisions on walks now instead of staring at a blank doc.

Anyway half of this is probably obvious to people who figured it out sooner.

Got lunch with a former coworker last week. She looked exhausted.

Apparently the creative agency we both worked at gutted their design team around March. Went from a full squad to basically one senior designer who now "manages" AI output all day. Leadership sold it as a restructure to stay competitive. Nobody bought that framing but nobody said anything either because the job market is brutal right now.

So now this one person reviews AI-generated mockups, fixes weird layout decisions the models keep making, argues with project managers about why the AI cant actually read a creative brief, and somehow ships more work than the old team did. At least on paper. The quality though. Clients are noticing stuff feels generic and samey but management keeps pointing at the throughput numbers like thats all that matters.

She said the worst part is everyone calls her role "elevated" now. Same pay, three peoples worth of cognitive load, and she spends half her day babysitting outputs that almost work but never quite do.

Might take up pottery or something. At least a bowl knows what shape its supposed to be.

For about six months I was stuck in this loop where I'd spend 20 minutes crafting the perfect prompt, get mediocre output, tweak it, get slightly different mediocre output, repeat until I gave up. I work with AI APIs daily and I genuinely started questioning if I was just bad at this.

The thing that broke the cycle was completely accidental. I was debugging a pipeline at like 2am and accidentally sent the model's own output back to it with a system message that basically said "you are a senior reviewer, find every weakness in this draft." The response it gave back was brutal. It caught stuff I wouldn't have noticed, tone issues, logical gaps, assumptions that didn't hold up.

So I started doing it deliberately. Generate first, don't overthink the initial prompt. Then pass the output through what I call a "critic pass" where the model reviews its own work against specific criteria. Then a third call to rewrite based on the criticism. Three calls instead of one, costs a bit more, but the difference in output quality is night and day.

The counterintuitive part is that the first prompt barely matters anymore. I write something rough, almost lazy, and let the iteration do the heavy lifting. My initial prompts went from paragraphs of instructions to like two sentences.

Still refining the critic criteria for different use cases but this fundamentally changed how I think about working with these models.

Had a final round yesterday for a mid-level backend role. The coding portion was what you'd expect, system design-ish problem, nothing crazy. But the interviewer straight up told me I could use whatever AI tools I wanted during the session.

I figured it was a trap so I started writing things out manually. He stopped me maybe 5 minutes in and said something like "I want to see how you work with these tools, not without them." So I pulled up an assistant, started prompting, and he was way more interested in how I validated the output and caught the bugs than whether I could write the function from scratch.

The whole thing took about 40 minutes and honestly it felt like a completely different skill than what I've been practicing. I spent the last 3 months grinding problems every night and none of that mattered. What mattered was whether I could spot when the AI gave me garbage and fix it quickly.

I don't even know how to feel about it. Part of me thinks this makes way more sense for actual day to day work. The other part of me is like, cool, so all those hours were just wasted then.

Curious if anyone else has run into this. Starting to wonder if the whole leetcode grind meta is about to become irrelevant or if this was just one weird company.

Friend of mine works at a mid-size SaaS company. They started rolling out AI agents for code review, testing, even writing basic features about 6 months ago. First it was "just helping the team move faster." Then the layoffs started quietly.

They lost 8 people. The ones left are basically babysitting AI output all day, fixing hallucinated code and rewriting tests that look right but test nothing. Management calls them "AI-augmented engineers" now which apparently means doing 3x the work for the same pay while pretending to be grateful.

The wild part is nobody pushed back because they were all scared of being next. So they just kept saying yeah this is great, so much more productive. Meanwhile the codebase is slowly turning into spaghetti that nobody fully understands because half of it was generated by something that doesnt actually understand what it wrote.

I keep hearing stories like this from people I know and honestly starting to wonder if we're all just watching this happen in slow motion. Thinking about picking up woodworking as a backup plan, at least a table cant be hallucinated.