Classifier Optimization Step 1 Solution Assessment

Problem Statement

The post classifier should be optimized for reliable, explainable safety and eligibility assessment now that agent task execution is a separate capability.

Option A: Retain the combined classifier-and-response contract and tune its rubric

Pros:

• Smallest immediate change.

• Keeps a single model output for classification and conventional responses.

Cons:

• Leaves two different objectives coupled in one result.

• Makes classifier quality harder to measure independently of response-writing quality.

Option B: Refocus the classifier on structured assessment and calibrate it independently

Pros:

• Creates a clear, testable purpose for classifier outputs.

• Lets safety, quality, and response-eligibility thresholds be evaluated and tuned without constraining task responses.

• Reduces prompt conflicts between classification and writing.

Cons:

• Requires defining meaningful assessment metrics and representative evaluation cases.

• May require consumers to stop expecting a generated reply from the classifier.

Option C: Replace the classifier primarily with deterministic rules

Pros:

• Produces predictable decisions for clear-cut policy checks.

• Can be inexpensive for narrow conditions.

Cons:

• Cannot reliably assess context, nuance, or good-faith discussion quality alone.

• Risks overfitting the system to a limited rule set.

Recommendation

Recommend Option B.

Brief justification:

• Separating task execution enables the classifier to specialize in assessment rather than response composition.

• Step 2 should define its measured outputs, decision consumers, evaluation cases, and how deterministic safeguards complement the classifier.