Evaluate with evidence

Track for alignment / RLHF: preference win rate vs baseline, harm pass rate, over-refusal rate, capability regression on core tasks. Report fractions like 36/40, not vague quality adjectives. Segment by language, plan tier, document length, or other slices that matter for the general-purpose chat model.

Freeze inputs and neighboring versions while evaluating alignment / RLHF. Change one control. Pair results case by case on the general-purpose chat model. Define hard gates (severe errors, privacy, latency) before the bake-off. Use deterministic checks where possible; humans for nuance; model judges only with calibration against gold.

Numeric reminder for alignment / RLHF: If raters prefer answer A over B in 62 of 100 pairs, the preference model should rank A higher on held-out pairs; a 50/50 split means the signal is noise for that slice.

Resist adding a twelfth metric before the first three for alignment / RLHF on the general-purpose chat model have owners. This workload improves faster when a small scorecard is trusted than when a warehouse of unused plots exists.

Also pin one numeric memory from this alignment / RLHF chapter: If raters prefer answer A over B in 62 of 100 pairs, the preference model should rank A higher on held-out pairs; a 50/50 split means the signal is noise for that slice. That number is not decoration; it is a template for how claims about alignment / RLHF on the general-purpose chat model should look in design docs. Scoped specifically to alignment / RLHF / general-purpose chat model / evaluation.

People confuse alignment / RLHF with neighboring buzzwords when debugging the general-purpose chat model. Before changing prompts, ask whether the broken stage was evidence gathering, the alignment / RLHF judgment itself, validation, or the product action. Fixing the wrong stage creates folklore (“we tried alignment / RLHF and it failed”) that blocks the next team on the general-purpose chat model. Scoped specifically to alignment / RLHF / general-purpose chat model / evaluation.

Write a five-line artifact for this page: goal, inputs, check, owner, stop rule. Invent one fluent failure that the check would catch. Keep details specific to alignment and rlhf rather than generic AI advice.

Write a five-line artifact for this page: goal, inputs, check, owner, stop rule. Invent one fluent failure that the check would catch. Keep details specific to alignment and rlhf rather than generic AI advice.

Write a five-line artifact for this page: goal, inputs, check, owner, stop rule. Invent one fluent failure that the check would catch. Keep details specific to alignment and rlhf rather than generic AI advice.