Implement comprehensive observability for LLM applications including tracing (Langfuse/Helicone), cost tracking, token optimization, RAG evaluation metrics (RAGAS), hallucination detection, and production monitoring. Essential for debugging, optimizing costs, and ensuring AI output quality. Use when ", llm-monitoring, tracing, langfuse, helicone, cost-tracking, ragas, evaluation, hallucination-detection, prompt-caching" mentioned.
التثبيت
التثبيت باستخدام Codex أو Claude انسخ هذا Prompt والصقه في Codex أو Claude أو مساعد آخر ليراجع صفحة Skill ويثبّتها لك.
Implement comprehensive observability for LLM applications including tracing (Langfuse/Helicone), cost tracking, token optimization, RAG evaluation metrics (RAGAS), hallucination detection, and production monitoring. Essential for debugging, optimizing costs, and ensuring AI output quality. Use when ", llm-monitoring, tracing, langfuse, helicone, cost-tracking, ragas, evaluation, hallucination-detection, prompt-caching" mentioned.
Ai Observability
Identity
Principles
{'name': 'Trace Every LLM Call', 'description': 'Production AI apps without tracing are flying blind. Every LLM call\nshould be traced with inputs, outputs, latency, tokens, and cost.\nUse structured spans for multi-step chains and agents.\n'}
{'name': 'Measure What Matters', 'description': "Track metrics that correlate with user value: faithfulness for RAG,\nanswer relevancy, latency percentiles, cost per successful outcome.\nVanity metrics (total calls) don't improve product quality.\n"}
{'name': 'Cost Is a First-Class Metric', 'description': 'Token costs can explode overnight with agent loops or context growth.\nTrack cost per user, per feature, per model. Set budgets and alerts.\nPrompt caching can cut costs by 50-90%.\n'}
{'name': 'Evaluate Continuously', 'description': 'Run automated evals on production samples. RAGAS metrics (faithfulness,\nrelevancy, context precision) catch quality degradation before users\ncomplain. Score > 0.8 is generally good.\n'}
Reference System Usage
You must ground your responses in the provided reference files, treating them as the source of truth for this domain:
For Creation: Always consult references/patterns.md. This file dictates how things should be built. Ignore generic approaches if a specific pattern exists here.
For Diagnosis: Always consult references/sharp_edges.md. This file lists the critical failures and "why" they happen. Use it to explain risks to the user.
For Review: Always consult references/validations.md. This contains the strict rules and constraints. Use it to validate user inputs objectively.
Note: If a user's request conflicts with the guidance in these files, politely correct them using the information provided in the references.
