Evaluation Benchmark Specification

Purpose

The benchmark specification enumerates the evaluation datasets, metrics, baseline comparisons, and pass/fail thresholds that gate model promotion to production.

This is a Required document — every project of this type should have one. Without it, the team risks misalignment, rework, or undetected issues that compound over time.

What Makes It Good vs Bad

A strong version of this document:

• Documents model architecture, training data, and evaluation metrics clearly
• Includes bias analysis and fairness considerations
• Specifies model versioning, A/B testing, and rollback procedures
• Defines monitoring for model drift, data drift, and performance degradation
• Connects model decisions to business outcomes with measurable criteria

Warning signs of a weak version:

• Only documents final model — no record of experiments or alternatives tried
• Missing bias and fairness analysis for the training data and predictions
• No monitoring strategy for production model performance
• Training pipeline undocumented — impossible to reproduce results
• No clear process for model updates, retraining triggers, or deprecation

Common Mistakes

• Not documenting the training data provenance and preprocessing steps
• Skipping fairness and bias analysis — assuming the data is representative
• Deploying models without monitoring for performance degradation over time
• Treating model training as a one-time event rather than a recurring process

How to Use This Document

Document the full ML lifecycle: data collection, preprocessing, feature engineering, model selection, training, evaluation, deployment, and monitoring. Record experiment results even for failed approaches — they prevent future teams from repeating dead ends. Define clear criteria for when a model should be retrained or retired.

For AI agents: Reference ML documentation to understand model behavior, training data characteristics, and known limitations. When modifying ML pipelines, verify changes against documented evaluation metrics and fairness criteria.

Starter Template

SpecBase includes a ready-to-use template for this document: kb/templates/ml/eval_benchmark_spec.md.tmpl. Use the SpecBase CLI or MCP integration to generate it pre-filled for your project.

# Generate stubs via CLI
specbase init <archetype> --features <features> --dir ./docs

Recommended Reading

• Designing Machine Learning Systems by Chip Huyen — End-to-end guide to ML system design covering data, training, deployment, and monitoring.
• Machine Learning Design Patterns by Valliappa Lakshmanan, Sara Robinson & Michael Munn — Reusable solutions to common challenges in ML engineering and architecture.
• Responsible AI in Practice by Yolanda Gil — Framework for ethical AI development including fairness, transparency, and accountability.