Add evals for all AI workflows

[monsieurBoutte]

Add evals for most AI workflows

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Description

Create an evaluation framework to measure output quality and consistency across our AI-powered workflows.

Background

We currently have integration tests for functional correctness, but lack evaluations (evals) that measure the quality of AI outputs. As we iterate on prompts, switch models, or add providers, we need a systematic way to detect regressions and compare performance.

Workflows to evaluate

Workflow	Output to evaluate
summarization	Title, description, and tag relevance/accuracy
moderation	Sexual/violence score precision vs ground truth
chapters	Chapter boundary accuracy and title quality
burned-in-captions	Caption rendering correctness
translate-captions	Translation accuracy and fluency
translate-audio	TBD

Acceptance Criteria

• Eval dataset fixtures with labeled examples (input assets + expected/rated outputs)
• Scoring functions per workflow type (e.g., similarity, precision, human-preference proxy)
• Support for comparing results across providers (OpenAI, Anthropic, etc.)
• CI-integrated eval runner with configurable pass/fail thresholds
• Eval results output (JSON report, markdown summary, or dashboard)

Suggested Approach

1. Start with summarization and moderation as pilot workflows
2. Use a small curated dataset (~3-10 examples per workflow)
3. Define simple metrics first (exact match, factuality, threshold accuracy)
4. Expand to LLM-as-judge for subjective quality

Related

• Existing integration tests: tests/integration/
• Function implementations: src/functions/