@pointlessone@status.pointless.one
Post #1868568
2026-04-02 07:22 UTC
@harpaa01 it only seems stupid if you think of it as role play.
If you frame every interaction with LLM as context priming it becomes pretty obvious. In what context in training data a certain kind of response is more likely? If you stuff your prompt with tokens that often go with good debugging you’re likely to get good debugging.
In a way, “prompt engineering” is a game of guessing training data, sort of a reverse engineering exercise.
Replies (1)
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@harpaa01@mastodon.social 2026-04-02 16:40
@pointlessone I get why it works, but it feelslike a bad architecture to put things structural to your actual product into the prompt, where 1) behavior is not deterministic, and 2) you increase the chances not every instruction will be followed to the letter the more you cram in there (plus the fact that your prompt has limits to how big it can be). It feels like we don't try to build the models to be more specialized because we either don't know how, or we know it'd be really expensive.