this post was submitted on 06 Jan 2026
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Programming
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When writing code, I don't let AI do the heavy lifting. Instead, I use it to push back the fog of war on tech I'm trying to master. At the same time, keep the dialogue to a space where I can verify what it's giving me.
About 20% of the time, it'll suggest things that are entirely plausible and probably should exist, but don't. Some platforms and APIs really do have barn-door-sized holes in them and it's staggering how rapidly AI reports a false positive in these spaces. It's almost as if the whole ML training stratagem assumes a kind of uniformity across the training set, on all axes, that leads to this flavor of hallucination. In any event, it's been helpful to know this is where it's most likely to trip up.
Edit: an example of one such API hole is when I asked ChatGPT for information about doing specific things in Datastar. This is kind of a curveball since there's not a huge amount online about it. It first hallucinated an attribute namespace prefix of
data-star-which is incorrect (it usesdata-instead). It also dreamed up a JavaScript-callable API parked on a non-existentDatastar.object. Both of those concepts conform strongly to the broader world of browser-extending APIs, would be incredibly useful, and are things you might expect to be there in the first place.My problem with this, if I understand correctly, is I can usually do all of this faster without having to lead a LLM around by the nose and try to coerce it into being helpful.
That said, search engines do suck ass these days (thanks LLMs)
That's been my biggest problem with the current state of affairs. It's now easier to research newer tech through an LLM than it is to play search-result-wack-a-mole, on the off chance that what you need is on a forum that's not Discord. At least an AI can mostly make sense of vendor docs and extrapolate a bit from there. That said, I don't like it.
People will literally do anything to avoid rtfm
It's a struggle even finding the manual these days if you don't already know where it is / what it's called. I was searching about an issue with my car recently and like 90% of the results are generic AI-generated "How to fix ______" with no actual information specific to the car I'm searching for.
I searched up a video to replace a part on my car. I did find it, but I also found 15 videos that were AI generated product reviews of the part.
I definitely also want my car parts to be "sleek and stylish" when hidden away under a plastic cover under the hood lmao
I find it best to get the agent into a loop where it can self-verify. Give it a clear set of constraints and requirements, give it the context it needs to understand the space, give it a way to verify that it's completed its task successfully, and let it go off. Agents may stumble around a bit but as long as you've made the task manageable it'll self correct and get there.
I like your strategy. I use a system prompt that forces it to ask a question if there are options or if it has to make assumptions. Controlling context is key. It will get lost if it has too much, so I start a new chat frequently. I also will do the same prompts on two models from different providers at the same time and cross reference the idiots to see if they are lying to me.
I'm kind of amazed that even works. I'll have to try that. Then again, I've asked ChatGPT to "respond to all prompts like a Magic 8-ball" and it knocked it out of the park.
I do this as well, and totally forgot to mention it. Yes, I keep the context small and fresh so that prior conversations (and hallucinations) can't poison new dialogues.
Oooh... straight to my toolbox with that one. Cheers.
I forgot another key. The code snippets they give you are bloated and usually do unnecessary things. You are actually going to have to think to pull out the needed line(s) and clean it up. I never copy paste.