Comment by citizenpaul
2 days ago
>makes the bot follow orders with greater precision.
Gemini will ignore any directions to never reference or use youtube videos, no matter how many ways you tell it not to. It may remove it if you ask though.
2 days ago
>makes the bot follow orders with greater precision.
Gemini will ignore any directions to never reference or use youtube videos, no matter how many ways you tell it not to. It may remove it if you ask though.
Positive reinforcement works better that negative reinforcement. If you the read prompt guidance from the companies themselves in their developer documentation it often makes this point. It is more effective to tell them what to do rather than what not to do.
This matches my experience. You mostly want to not even mention negative things because if you write something like "don't duplicate existing functionality" you now have "duplicate" in the context...
What works for me is having a second agent or session to review the changes with the reversed constraint, i.e. "check if any of these changes duplicate existing functionality". Not ideal because now everything needs multiple steps or subagents, but I have a hunch that this is one of the deeper technical limitations of current LLM architecture.
Probably not related but it reminds me of a book I read where wizards had Additive and Subtractive magic but not always both. The author clearly eventually gave up on trying to come up with creative ways to always add something for solutions after the gimmick wore off and it never comes up again in the book.
Perhaps there is a lesson here.
Could you describe what this looks like in practice? Say I don't want it to use a certain concept or function. What would "positive reinforcement" look like to exclude something?
Instead of saying "don't use libxyz", say "use only native functions". Instead of "don't use recursion", say "only use loops for iteration".
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