Generate Evaluator

2) Command

--evaluator-type command

• Generates a bash evaluator scaffold.
• Best for deterministic local checks and CI/offline workflows.

3) HTTP

--evaluator-type http

• Generates a Python HTTP server evaluator scaffold.
• Useful when evaluator needs to run as a service.

4) Composite

--evaluator-type composite

• Generates a Python evaluator with:
  1. hard deterministic constraints (fast gate), then
  2. LLM judge scoring only if constraints pass.
• Constraint failure returns score: 0.0.

Generation Flags

• --evaluator-type judge|command|http|composite
• --model <litellm-model>: hardcodes judge model into judge/composite scripts.
• --dataset: generate dataset-aware templates that read example and show how to use it in scoring.
• --intake-json / --intake-file: embed rubric/quality dimensions.

Quick Start

Generate a judge evaluator and test it:

# Generate
optimize-anything generate-evaluator seed.txt \
  --objective "Score clarity and specificity" \
  --model openai/gpt-4o-mini > eval_judge.py

# Test it
echo '{"candidate":"Your artifact text here"}' | python3 eval_judge.py

This returns JSON like:

{"score": 0.82, "reasoning": "Clear structure but lacks examples", "clarity": 0.9, "specificity": 0.7}

For dataset-aware evaluators:

optimize-anything generate-evaluator seed.txt \
  --objective "Score correctness" \
  --dataset examples.jsonl > eval_dataset.py

echo '{"candidate":"text","example":{"input":"q","expected":"a"}}' | python3 eval_dataset.py

Workflow

1. Clarify artifact + objective + hard constraints.
2. Pick evaluator pattern (judge default, composite for safety gates).
3. Run generator to scaffold.
4. Customize scoring logic and side-info fields.
5. Test with stdin payloads. You should see JSON with score plus diagnostic fields.
6. Validate score range: a good seed should score between 0.3-0.7. If above 0.85, the evaluator lacks discrimination.