LLM & Agentic

Metrics Framework

Observability metrics for LLM applications: latency, token usage, cost tracking, and quality scoring dashboards.

Last updated 2026-06-11

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.

WHICH EVAL SECTION IS THIS?

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.

Observability Pipeline Architecture

LLM Lifecycle

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Complete lifecycle of large language models from pre-training through fine-tuning, RLHF, and deployment — with architecture diagrams and production considerations.

How LLMs Call Tools

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How 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

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Should you fine-tune at all? A structured decision framework for prompt engineering vs RAG vs fine-tuning.

LoRA, QLoRA & PEFT Methods

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How LoRA works inside transformer layers, QLoRA for memory-efficient training, and the full PEFT method comparison with code examples.

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Observability: Complete Metrics Framework

More in LLM & Agentic

LLM Lifecycle

How LLMs Call Tools

When to Fine-Tune: The Decision Framework

LoRA, QLoRA & PEFT Methods