LocalLLaMA thread probes prompt drift

I think the core observation is real, but the framing is a little too neat: what holds better is usually whatever is easiest for the model or runtime to verify, not prohibitions as a special class.

• –Long contexts dilute salience, and models lean on recency plus training priors; verbose style guidance is easier to ignore than binary rules.
• –Negative constraints often map to simpler checks, like length caps, JSON-only output, or no extra commentary, so they survive drift better in practice.
• –Safety and instruction-tuning likely bias models toward respecting refusal-style language, which can make “don’t” constraints feel disproportionately strong.
• –For production agents, the durable pattern is layered enforcement: concise instructions, schemas, validators, and post-checks instead of one giant prompt.
• –Reasserting a few critical constraints every turn usually beats adding more prose once the context gets crowded.