Comment by dwa3592
2 hours ago
This sounds like another version of "As a chat becomes longer, the guardrails seem to become fuzzy". You can't use all of the context window bc at the end, the output would not respect the constraints (or guardrails) but to reliably produce production grade code you want the model to have expansive awareness which fills up the context window pretty quickly. It's like saying "Keep everything in mind from these 6 directories - and make this <insert ticket> change" - but keeping everything in mind already fills it's context window which makes it lose it's ability to follow the constraints (or guardrails).
This is not a new problem though. This is why we started writing modular code, strict interfaces etc
And doing incremental dev, so once a feature is done you can mostly ignore it.
If there is one good thing that the generative AI tools have shown beyond any doubt it's that the classic "good programming" practices are still useful and effective. Self-documenting code. Modular design. Clearly defined architecture. Incremental development. Coding standards. Automated tests. Automated everything.
If there's a second thing the generative AI tools have shown beyond any doubt it's that many of the more modern (relatively speaking) "best practices" that have always been over-hyped and questionably-evidenced really do tend to produce worse results. LLMs take these methods to their logical conclusions and show us the end result much sooner. You can't just iterate your way to a solution when you don't even know what problem you're trying to solve. If you don't have a clear spec then you don't know what a correct product looks like. You need to invest time in reviewing code properly. If you don't keep the big picture in mind then the big picture becomes a mess.
Maybe one day the LLMs will leave me out of a job but at least I'll feel validated first!