Metrics Framework
Observability metrics for LLM applications: latency, token usage, cost tracking, and quality scoring dashboards.
Last updated
Observability: Complete Metrics Framework
You can't improve what you don't measure. Here's every metric category, how to calculate each, and how it drives decisions.
This is the production metrics reference — what to graph, how to compute each metric, and what to alert on. For the eval process (golden datasets, LLM-as-judge, A/B tests, CI regression) see Eval & Observability: The Full Stack. For grading agent trajectories specifically, see Grading Agents: Agentic Evaluation.
Related
More in LLM & Agentic
LLM Lifecycle
PreviewComplete lifecycle of large language models from pre-training through fine-tuning, RLHF, and deployment — with architecture diagrams and production considerations.
How LLMs Call Tools
PreviewHow LLMs use function calling and tool use — the mechanics behind tool-calling agents, from prompt engineering to structured output.
When to Fine-Tune: The Decision Framework
PreviewShould you fine-tune at all? A structured decision framework for prompt engineering vs RAG vs fine-tuning.
LoRA, QLoRA & PEFT Methods
PreviewHow LoRA works inside transformer layers, QLoRA for memory-efficient training, and the full PEFT method comparison with code examples.
Get full access to all 87+ sections with code examples, diagrams, and interactive animations.
Unlock Premium