The LLM Preamble Problem: How RLHF Made Your Model Too Polite to Ship
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TL;DR Every instruction-tuned model has a preamble habit: it opens with "Certainly!", "Great question!", or "Of course!" before answering you. RLHF is the root cause. Human raters rewarded warm, thorough responses during training. The model learned that politeness signals quality. Each preamble adds 10 to 30 tokens of pure waste. At 1 million interactions per month, that burns $200 in API budget before your users see a single useful word. Four fixes exist. System-prompt suppression, structured…
1Key Takeaways
- TL;DR Every instruction-tuned model has a preamble habit: it opens with "Certainly!", "Great question!", or "Of course!" before answering you.
- Human raters rewarded warm, thorough responses during training.
- The model learned that politeness signals quality.
- Each preamble adds 10 to 30 tokens of pure waste.
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3Why it matters
Coding AI shifts how fast software ships and how much human review each change needs. DEV — AI reports that tL;DR Every instruction-tuned model has a preamble habit: it opens with "Certainly!", "Great question!", or "Of course!" before answering you.
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