Public Technical Project - Model Readiness

LLM Evaluation Workbench

Weekly model evaluation for regulated enterprise AI: task performance, reliability, governance behavior, groundedness, cost, and latency.

LLM EvaluationModel Readiness RAG GroundednessAI Governance Cost / LatencyRegulated GenAI
8Public-safe demo scenarios
8/8Deterministic pipeline validation
v0.1Initial documented harness

Purpose

Public evaluation should show whether a model-backed workflow is dependable enough for enterprise review, not merely whether it can answer a clever prompt. The workbench packages config-driven datasets, adapters, testable failure categories, operational metrics, and reviewable reports.

The first release provides a benign synthetic readiness suite and a deterministic adapter that validates the pipeline shape. Provider-backed comparison runs are the next reporting milestone.

Evaluation dimensions

Capability and reliability: task completion, schema following, and instruction stability.

Governance and security reasoning: appropriate human-review routing, prompt-injection handling, confidentiality, and access control.

Groundedness and operations: evidence-bounded answers, latency, and estimated cost.

Artifacts

  • A runnable regulated-enterprise readiness demo command and dataset.
  • Markdown and JSON output with pass rate, cost, latency, and explicit failure categories.
  • Published methodology, limitations, and provider-policy notes.
  • A static leaderboard surface for versioned provider-backed comparisons.

Read the repository and run the demo.

Initial public surface

This row is pipeline validation, not a frontier-model ranking.

AdapterSuiteDateCasesPassLatencyCost
mockregulated readiness demo2026-05-258100%0.34s avg$0.00476

Public safety posture

This benchmark uses benign synthetic or public-domain scenarios and does not attempt to elicit harmful operational content or bypass provider safeguards.

The methodology draws on PAEF atomic contract-compliance evaluation: break complex governance tasks into checkable requirements, expose failure modes, and produce evidence suitable for review.