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Observability for LLM Applications: Metrics That Matter

Key metrics, dashboards and alerts to monitor LLM apps in production and ensure reliability.

HR
Harish R.
AI Engineer, Botmartz · April 30, 2024 · 8 min read
◈ Open in Google Colab⌥ View Code on GitHub
Read Time
8 min
Failure Modes
5
Code Snippets
3
Runnable Notebook
1
Botmartz AI Insight
Evaluating Retrieval, Chunking, and Generation in Production
Retrieval QualityChunking StrategyHybrid SearchContinuous EvaluationGuardrailsTakeaways
# Observability for LLM Applications: Metrics That Matter Deploying LLMs to production is only the beginning. Keeping them running reliably, accurately, and cost-effectively requires comprehensive observability. ## Key Metrics to Monitor 1. **Latency**: Time to first token (TTFT) and total response latency. 2. **Tokens**: Input vs output token counts to monitor cost and usage patterns. 3. **Accuracy / Hallucination Rate**: Groundedness and relevance scores using LLM-as-a-judge. 4. **Error Rates**: Rate-limit exceptions, timeouts, and guardrail blocks.

Closing Takeaways

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Measure retrieval precision and recall in isolation before touching the model.
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Chunk along document structure, not arbitrary character counts.
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Combine vector and keyword search — hybrid retrieval beats either alone.
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Treat evaluation as continuous infrastructure, not a launch-week report.
Try It Yourself
A runnable Google Colab notebook with the eval harness and hybrid search code from this post.
◈ Open in Colab⌥ GitHub Repo
#Enterprise RAG#Evaluation#Production AI#LangChain
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HR
Harish R.
AI Engineer at Botmartz, building enterprise RAG and agent systems in production. Contributing to open-source libraries.

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