Skip to main content
Botmartz AI Solution PVT LTD.
Botmartz AI Solution PVT LTD.
Home›Engineering Insights›MLOPS
MLOPS

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

✓
Measure retrieval precision and recall in isolation before touching the model.
✓
Chunk along document structure, not arbitrary character counts.
✓
Combine vector and keyword search — hybrid retrieval beats either alone.
✓
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
0 views
HR
Harish R.
AI Engineer at Botmartz, building enterprise RAG and agent systems in production. Contributing to open-source libraries.

Discussion (0)

No approved comments yet. Be the first to share your thoughts!

Leave a Comment

Your email address will not be published. Required fields are marked *

More Engineering Insights
ENTERPRISE RAGBuilding a Scalable Enterprise RAG System: Architecture, Components & Best Practices
Botmartz Engineering Team · 12 min read
AI AGENTSInside AI Agents: From Reasoning to Real-World Action
Soham Sharma · 9 min read
ARCHITECTUREMicroservices vs Monolith for AI Systems: What We Learned
Renee D. · 7 min read
ENTERPRISE RAGAdvanced RAG Techniques: Hybrid Search, Reranking & Query Rewriting
Botmartz Team · 10 min read
AI AGENTSBuild an AI Chatbot with LangGraph and OpenAI: Step-by-Step
Botmartz Engineering Team · 11 min read
OPEN SOURCEOpen Source Projects from Botmartz Labs You Should Know
Botmartz Engineering Team · 6 min read
AI ENGINEERINGPrompt Engineering Patterns That Actually Work in Production
Botmartz Engineering Team · 8 min read
CASE STUDIESHow We Built a Legal Document Intelligence System for 100K+ Docs
Botmartz Engineering Team · 14 min read