# Quality, Cost & Latency Measurement

> Task success, calibrated quality, token and tool cost, latency distributions, reliability, and Pareto frontiers.

Canonical: https://learn.significanthobbies.com/curriculum/concepts/quality-cost-latency-measurement

Task success, calibrated quality, token and tool cost, latency distributions, reliability, and Pareto frontiers.

- Difficulty: core
- Tracks: Evaluation & AI Reliability

## Mental model

No AI metric stands alone. Compare candidate systems on the same workload and plot quality, cost, latency, and failure rate together. Scope: this card owns comparing candidate SYSTEMS on one workload — the Pareto frontier across quality, token and tool cost, latency distribution, and failure rate. Scoring an individual output is `llm-evals`; gating a release on the result is `ai-regression-testing`.



## Primary sources

- [MLPerf Inference Benchmark](https://arxiv.org/abs/1911.02549) (doc)
- [Chatbot Arena: An Open Platform for Evaluating LLMs by Human Preference](https://arxiv.org/abs/2403.04132) (doc)
- [The Tail at Scale](https://research.google/pubs/the-tail-at-scale/) (paper)
- [HELM — Holistic Evaluation of Language Models (Stanford CRFM)](https://crfm.stanford.edu/helm/) (article)
- [Site Reliability Engineering — Service Level Objectives](https://sre.google/sre-book/service-level-objectives/) (doc)
- [MLPerf Inference](https://mlcommons.org/benchmarks/inference-datacenter/) (doc)

## Practice

### Design exercise: Quality, Cost & Latency Measurement

Task success, calibrated quality, token and tool cost, latency distributions, reliability, and Pareto frontiers. Implement designOutline() returning non-empty values for: qualityMetric, costMetric, latencyMetric. Each value must name a concrete mechanism or decision.

**Expected evidence:** A design outline with qualityMetric, costMetric, latencyMetric plus an explicit failure mode or trade-off.

## Review prompts

- Why is "config A is better than config B" usually an unanswerable question?

## Build evidence

- **Synthesize: Evaluation & AI Reliability** — Build an evidence-backed evaluation and observability system for models, tools, and agents. Produce one working system, benchmark, or evidence-backed design that integrates the path.

## Prerequisites

- [Hallucination & Failure Detection](https://learn.significanthobbies.com/curriculum/concepts/hallucination-failure-detection)

## Related concepts

- [Hallucination & Failure Detection](https://learn.significanthobbies.com/curriculum/concepts/hallucination-failure-detection)
- [Agent Observability](https://learn.significanthobbies.com/curriculum/concepts/agent-observability)

## Learning paths

- [12-Week Evaluation & AI Reliability](https://learn.significanthobbies.com/curriculum/roadmaps/ai-reliability-12w)