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* Add 9 Arize LLM observability skills Add skills for Arize AI platform covering trace export, instrumentation, datasets, experiments, evaluators, AI provider integrations, annotations, prompt optimization, and deep linking to the Arize UI. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> * Add 3 Phoenix AI observability skills Add skills for Phoenix (Arize open-source) covering CLI debugging, LLM evaluation workflows, and OpenInference tracing/instrumentation. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> * Ignoring intentional bad spelling * Fix CI: remove .DS_Store from generated skills README and add codespell ignore Remove .DS_Store artifact from winmd-api-search asset listing in generated README.skills.md so it matches the CI Linux build output. Add queston to codespell ignore list (intentional misspelling example in arize-dataset skill). Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> * Add arize-ax and phoenix plugins Bundle the 9 Arize skills into an arize-ax plugin and the 3 Phoenix skills into a phoenix plugin for easier installation as single packages. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> * Fix skill folder structures to match source repos Move arize supporting files from references/ to root level and rename phoenix references/ to rules/ to exactly match the original source repository folder structures. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> * Fixing file locations * Fixing readme --------- Co-authored-by: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
41 lines
1.1 KiB
Markdown
41 lines
1.1 KiB
Markdown
# Evaluators: Overview
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When and how to build automated evaluators.
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## Decision Framework
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```
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Should I Build an Evaluator?
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│
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▼
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Can I fix it with a prompt change?
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YES → Fix the prompt first
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NO → Is this a recurring issue?
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YES → Build evaluator
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NO → Add to watchlist
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```
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**Don't automate prematurely.** Many issues are simple prompt fixes.
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## Evaluator Requirements
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1. **Clear criteria** - Specific, not "Is it good?"
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2. **Labeled test set** - 100+ examples with human labels
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3. **Measured accuracy** - Know TPR/TNR before deploying
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## Evaluator Lifecycle
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1. **Discover** - Error analysis reveals pattern
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2. **Design** - Define criteria and test cases
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3. **Implement** - Build code or LLM evaluator
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4. **Calibrate** - Validate against human labels
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5. **Deploy** - Add to experiment/CI pipeline
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6. **Monitor** - Track accuracy over time
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7. **Maintain** - Update as product evolves
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## What NOT to Automate
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- **Rare issues** - <5 instances? Watchlist, don't build
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- **Quick fixes** - Fixable by prompt change? Fix it
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- **Evolving criteria** - Stabilize definition first
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