In an internal experiment, outputs using logit bias were judged "less AI-like" than the baseline 74.5% of the time over 353 blind A/B evaluations. Logit bias is a vocabulary knob. You push specific tokens down (ban) or up (boost) during generation. It works on tokens, not
A PASS/FAIL eval isn’t actionable unless it tells you WHY the judge evaluated it as such. Ensure every binary LLM-as-judge evaluation returns REASONING and EVIDENCE alongside the DECISION. RED Framework = (Reasoning + Evidence + Decision) This allows you to identify failures in seconds and improve your prompts and models faster