But that is an open question. For example some smaller, local-friendly LLMs (Gemma, Qwen) clearly do better without a long system prompt. Faster ad just no worse at code. Both seem eminently capable of writing high quality, eloquent code without being specifically prompted to, both need little instruction on tool calling. The consensus is leading us towards very long system prompts, agent files, stuffing with skills, when it might be cargo-culting (again).
A culture of really long preambles certainly benefits cloud AI companies though!
You can just ask any model to go do a code review and it will probably do better than no review at all but with rules you can make sure at least it processed inputs enough to produce a hallucinated reason which you can check for why a rule doesn't apply.
To me, that's worth losing some theoretical model performance and token efficiency unless you're using the small local models which basically lose all of that while trying to follow the rules.
But that is an open question. For example some smaller, local-friendly LLMs (Gemma, Qwen) clearly do better without a long system prompt. Faster ad just no worse at code. Both seem eminently capable of writing high quality, eloquent code without being specifically prompted to, both need little instruction on tool calling. The consensus is leading us towards very long system prompts, agent files, stuffing with skills, when it might be cargo-culting (again).
A culture of really long preambles certainly benefits cloud AI companies though!
You can just ask any model to go do a code review and it will probably do better than no review at all but with rules you can make sure at least it processed inputs enough to produce a hallucinated reason which you can check for why a rule doesn't apply.
To me, that's worth losing some theoretical model performance and token efficiency unless you're using the small local models which basically lose all of that while trying to follow the rules.