Test Harness

Benched.ai Editorial Team

A test harness is the framework that automates running prompts through a model, collecting outputs, and computing metrics for evaluation.

Harness Components

Component	Function	Example Tool
Runner	Dispatch prompts to model endpoints	OpenAI Evals CLI
Sampler	Select prompt subsets	Stratified random
Scorer	Compare output vs ground truth	EM, BLEU
Reporter	Generate dashboards	Superset, Grafana

Execution Flow

Load benchmark dataset.
Generate or fetch model completions.
Apply scorers and aggregate metrics.
Persist results with metadata (model version, timestamp).

Design Trade-offs

Local harness gives reproducibility; remote SaaS scales but may vary.
Caching results speeds re-runs but can hide regressions if environment changes.

Current Trends (2025)

Harnesses exporting OpenTelemetry spans for end-to-end timing¹.
YAML-based declarative test cases integrated into CI pipelines.

Implementation Tips

Seed RNG for deterministic temperature sampling during tests.
Store raw completions to enable manual review of failure cases.
Version scorer code along with datasets.

OpenTelemetry AI SIG, Standardizing LLM Benchmark Traces, 2025. ↩