The JSON shape in both prompts used real values as placeholders, and the model
was reading them as the answer:
instrument: 'NVDA' -> 397 of 611 predictions are NVDA (65%),
second place is LMT with 8
horizon_days: 10 -> 606 of 611 are horizon 10 (99.2%), out of
seven allowed horizons
direction: 'positive|negative' -> 502 of 611 are positive (82.2%)
event_type: 'stable_enum' -> the enum was never listed, so the model
invented one label per event, 201 distinct
values across 611 predictions
replayWorker had its own copy of the same prompt with the same values, which is
why both lanes show the identical skew (replay is 147/147 horizon 10, 138/147
NVDA).
Every placeholder is now a description of the field rather than a usable value,
with an explicit line saying not to copy them. event_type is validated against
the same closed family list the cohort key uses, so a label cannot mean one
thing in the prompt and another in calibration. Off-enum labels are salvaged
through the existing mapper when they are placeable and rejected when they are
not, so 'other' does not quietly become the bin again.
This does not by itself create edge. It means the next batch of predictions
measures the model's judgement instead of its willingness to copy an example.
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01WnNxwxfXSbeNtjvtz5gayb
