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Feedback lets you annotate completions with scalar values, boolean ratings, or pairwise preferences. Use feedback for monitoring, evaluation, and as training objectives.

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Register a feedback key

Before logging feedback, register a key:
adaptive.feedback.register_key(
    key="quality",
    kind="scalar",  # or "bool"
    scoring_type="higher_is_better",
)
ParameterTypeRequiredDescription
keystrYesUnique identifier
kindstrNo"scalar" (default) or "bool"
scoring_typestrNo"higher_is_better" (default) or "lower_is_better"

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Log metric feedback

Log a rating for a completion using its completion_id from the inference response:
# Get completion_id from inference
response = adaptive.chat.create(
    messages=[{"role": "user", "content": "Hello"}]
)
completion_id = response.choices[0].completion_id

# Log feedback
adaptive.feedback.log_metric(
    value=5,
    completion_id=completion_id,
    feedback_key="quality",
)
Feedback is associated with the completion’s Interaction record.

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Log preference feedback

Log a pairwise comparison between two completions:
adaptive.feedback.log_preference(
    feedback_key="quality",
    preferred_completion=completion_id_a,
    other_completion=completion_id_b,
)
Use preferences for RLHF/DPO training when you can judge which completion is better but can’t assign absolute scores.See SDK Reference for all feedback methods.