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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>
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Production: Guardrails vs Evaluators
Guardrails block in real-time. Evaluators measure asynchronously.
Key Distinction
Request → [INPUT GUARDRAIL] → LLM → [OUTPUT GUARDRAIL] → Response
│
└──→ ASYNC EVALUATOR (background)
Guardrails
| Aspect | Requirement |
|---|---|
| Timing | Synchronous, blocking |
| Latency | < 100ms |
| Purpose | Prevent harm |
| Type | Code-based (deterministic) |
Use for: PII detection, prompt injection, profanity, length limits, format validation.
Evaluators
| Aspect | Characteristic |
|---|---|
| Timing | Async, background |
| Latency | Can be seconds |
| Purpose | Measure quality |
| Type | Can use LLMs |
Use for: Helpfulness, faithfulness, tone, completeness, citation accuracy.
Decision
| Question | Answer |
|---|---|
| Must block harmful content? | Guardrail |
| Measuring quality? | Evaluator |
| Need LLM judgment? | Evaluator |
| < 100ms required? | Guardrail |
| False positives = angry users? | Evaluator |
LLM Guardrails: Rarely
Only use LLM guardrails if:
- Latency budget > 1s
- Error cost >> LLM cost
- Low volume
- Fallback exists
Key Principle: Guardrails prevent harm (block). Evaluators measure quality (log).