What Evaluation (evals) means in practice
Evaluation is the practice of systematically testing an AI system against a curated set of inputs and scoring the outputs on the dimensions the business actually cares about. For RAG: context precision, context recall, answer faithfulness, answer relevance. For classification: standard precision, recall, F1, calibration. For generation: a custom SME-built rubric scored either by humans or by a strong LLM as judge. The discipline that distinguishes a serious deployment from a demo is running the evals on every change — model update, prompt change, retrieval-pipeline change — and blocking deployment on regressions. Teams that ship without evals discover quality regressions in production; teams with evals catch them before merge.
Related terms
MLOps →
The discipline of taking ML and AI models from development through to reliable production operation — versioning, deployment, monitoring, evaluation, governance.
Drift (data + model) →
The phenomenon where a model's input distribution or its accuracy degrades over time as the world it predicts about changes.
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The Sovereign AI Playbook.
Enterprise generative AI that runs on your servers.
- 01Definition. The four non-negotiable properties of a sovereign deployment — and the three patterns that look sovereign but aren't.
- 02Why now. Three forces that converged in 2026: regulator pressure, the open-weights capability gap closing, and unit economics flipping.
- 03Reference architecture. The five-layer stack we deploy: applications → agent runtime → sovereign LLM → RAG + KB → data stores. With model and tooling choices for each.
- 04The regulators. SAMA · RBI · UK ICO · EU AI Act · DPDP · HIPAA — what each requires and how it maps to architecture.
All 62 terms, in plain language
Sovereign AI, RAG, agentic AI, IDP, MLOps and the regulations that shape enterprise AI.