Series Guide

AI Evals Content Map

A structured path through the evals cluster, from core concepts to deployment operations and business value.

This page is the front door to the evals section. It organizes the material by reading order and by operating concern so a reader can move from fundamentals into datasets, engineering workflow, production measurement, safety, governance, and executive outcomes without losing the thread.

What this section covers

Quality foundations What evals are, how they differ from testing, and how evaluation dimensions get defined.
Operational practice How teams design datasets, run build-time checks, evaluate RAG, and monitor live systems.
Control and value How safety, EvalOps, and business metrics turn evaluation into a real operating discipline.
Reading Order

Recommended path through the series

If someone is new to the topic, this sequence builds the right mental model first, then moves into implementation and production discipline.

1

What Are Evals?

The entry point for the whole cluster. It explains why AI quality requires more than deterministic testing and introduces the main evaluation dimensions.

2

Testing vs Evals

Clarifies where conventional testing stops and where probabilistic evaluation starts, so the rest of the series has sharper boundaries.

3

Enterprise QA Teams Need Evals, Not Just Tests

Translates the fundamentals into a practical operating shift for QA leaders who need to extend deterministic assurance into evals, rubrics, slices, and release evidence.

4

Pre-Build Evals for AI Agents

Frames evaluation as a design-time activity. This is where teams define behaviors, scenarios, rubrics, and failure modes before implementation begins.

5

Datasets, Golden Sets, and Scenario Design

Explains the substrate of good evals: representative examples, edge cases, adversarial cases, and versioned golden sets.

6

Build-Time Evals

Connects evaluation to engineering workflow through regression suites, CI/CD, release gates, and change review.

7

RAG Evals

Separates retrieval relevance, grounding, citation fidelity, and answer quality so RAG failures are diagnosable rather than hidden.

8

Runtime Evals and Observability

Moves the discussion into production behavior, traces, drift detection, anomalies, and learning from real usage.

9

Safety Evals and Red Teaming

Shows how to pressure-test systems for prompt injection, policy bypass, data leakage, unsafe tool use, and adversarial behavior.

10

EvalOps

Turns evaluation into an organizational operating model with ownership, versioned artifacts, sign-off, and drift management.

11

Business Metrics for Evals

Closes the loop by connecting evaluation to SLA adherence, cost-to-serve, productivity, CSAT, and ROI.

By Topic

How the content clusters together

Foundations

Conceptual base

Design and Engineering

Build the right system

Operations and Control

Run and govern it well
Coverage

What the current cluster now covers

The section now covers the full eval lifecycle with a practical bias toward agentic systems and enterprise deployment.

Area Primary article What it helps answer
Evals fundamentals What Are Evals? What evaluation is, why it exists, and which quality dimensions matter for AI systems.
Testing versus evaluation Testing vs Evals Which problems deterministic tests catch and which ones require probabilistic or rubric-based evaluation.
QA operating transition Enterprise QA Teams Need Evals, Not Just Tests How enterprise QA teams should extend deterministic assurance into evals, datasets, rubrics, slices, and release evidence for AI products.
Evaluation design before build Pre-Build Evals for AI Agents How to define scenarios, acceptance criteria, and failure modes before implementation starts shaping the requirements.
Datasets and scenario design Datasets, Golden Sets, and Scenario Design How to build representative, versioned, and risk-aware evaluation sets.
Build-time workflow Build-Time Evals How teams turn evals into engineering workflow with regression checks, release gates, and triage.
RAG-specific evaluation RAG Evals How to separate retrieval failure from grounding failure and final-answer failure.
Production behavior Runtime Evals and Observability How to detect drift, learn from traces, and evaluate performance after release.
Adversarial and safety pressure testing Safety Evals and Red Teaming How to test guardrails, prompt injection resistance, policy robustness, and unsafe tool behavior.
Governance and operating model EvalOps How teams own, version, approve, and review evaluation artifacts over time.
Business value Business Metrics for Evals How evaluation connects to cost, productivity, SLA adherence, containment, CSAT, and ROI.
Quick Starts

Fast entry points for different readers

If you are new to evals

If you lead QA or testing

If you are building AI systems

If you run production or governance