Use Case
Humanities & EQ
Judgment and values need calibrated evaluation.
Common failures
- Safe, hedged responses that avoid taking any position and end up useful to no one
- Ethical reasoning that applies Western defaults without acknowledging the frame
- Tone and empathy that sound right on the surface but miss what the person actually needs
BakeLens evaluates judgment quality
Domain experts score depth, nuance, and cultural calibration, not just fluency
Identify where the model hedges vs. where it should hedge, and where it gets the line wrong
Compare against expert baselines to separate style failures from reasoning failures
Proof delivers calibrated expert data
Annotations from humanities scholars, ethicists, and licensed practitioners
Rubrics that define what good judgment looks like in each subdomain, including art, ethics, and EQ
Cases where the right answer is genuinely ambiguous, labeled with expert reasoning about why
Deliverables
Judgment quality report
Where your model defaults to safe/generic, and where it misjudges nuance or tone
Expert-calibrated datasets
Hard cases in ethics, art criticism, and emotional reasoning, labeled by domain practitioners
Subjective eval framework
Rubrics and baselines for domains where there's no single right answer