Start here

Evaluating and measuring your AI

  • LLM evaluation How to evaluate a large language model: methods, a metrics table, LLM-as-a-judge, and RAG and agent evaluation.
  • AI agent evaluation Scoring agents that plan and call tools: tool-call correctness, trajectory evaluation, reliability, and benchmarks.
  • RAG evaluation Scoring retrieval-augmented systems: retrieval vs. generation metrics, the RAG triad, faithfulness, and golden datasets.
  • LLM-as-a-judge Using one model to score another: judge types, prompt and rubric design, the biases that break it, and validation.
  • How to measure AI ROI A CFO's framework for an AI ROI number you can defend, measured on your own data against an honest counterfactual.
  • Data readiness for AI What AI-ready data means, the dimensions that matter, a checklist to assess yours, and how to make it evaluable.
  • Golden datasets How to build the golden dataset that is your private benchmark: sourcing, sizing, labeling, versioning, and contamination.

The case for owned, private evaluation

Choosing an approach

  • Build vs. buy vs. own Build an evaluation capability in-house, buy a tool, or own one that a partner builds and transfers to you.
  • AI evaluation tools, compared How the categories of AI evaluation tool differ, and where an owned, business-outcome capability fits.
  • AI evaluation services A capability you own, not a dashboard you rent: how services, platforms, and build-operate-transfer differ.

Reference

  • AI evaluation glossary Plain definitions for the terms behind private, owned AI evaluation: golden datasets, ground truth, and more.