Observation from working with local LLMs in longer conversations.
When designing prompts, most approaches focus on adding instructions:
– follow this structure
– behave like X
– include Y, avoid Z
This works initially, but tends to degrade as the context grows:
– constraints weaken
– verbosity increases
– responses drift beyond the task
This happens even when the original instructions are still inside the context window.
What seems more stable in practice is not adding more instructions, but introducing explicit prohibitions:
– no explanations
– no extra context
– no unsolicited additions
These constraints tend to hold behavior more consistently across longer interactions.
Hypothesis:
Instructions act as a soft bias that competes with newer tokens over time.
Prohibitions act more like a constraint on the output space, which makes them more resistant to drift.
This feels related to attention distribution:
as context grows, earlier tokens don’t disappear, but their relative influence decreases.
Curious if others working with local models (LLaMA, Mistral, etc.) have seen similar behavior, especially in long-context or multi-step setups.
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