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Evaluation and benchmarks overview

**Evaluation** checks whether a system meets a stated goal. **Benchmarks** are shared tests the field uses to compare systems—useful, incomplete, and sometimes overfit. Anthropic’s *Challenges in evaluating AI systems* (2023) argues that...

What it is

Evaluation checks whether a system meets a stated goal. Benchmarks are shared tests the field uses to compare systems—useful, incomplete, and sometimes overfit. Anthropic’s *Challenges in evaluating AI systems* (2023) argues that many suites are limited as indicators of real capability or safety, and that governance depends on meaningful evals.

HIGH PRIORITYDIAGRAM
◷ IN PRODUCTION

Visual Spec & Architecture Diagram

Eval taxonomy tree: Offline benchmarks → Human eval → Model-as-judge → Online A/B → Safety suites → Red team. Axes: cheap↔expensive, proxy↔real task.

Educational Focus: Orients the entire eval course.

Why it matters

Leaderboards can mislead buyers and builders. Good eval mixes public benchmarks, private task tests, human review, and production metrics. NIST’s AI Resource Center (AIRC) frames TEVV—test, evaluation, verification, and validation—as how organizations check whether systems behave as claimed under changing conditions.

How it works (plain)

  1. Define success for *your* use case
  2. Build a golden set you control
  3. Track regressions when prompts/models change
  4. Use public benchmarks as context—not as the only score
  5. Separate capability checks from safety / policy checks
  6. Red-team for abuse categories (literacy in later units; Course 29 for harm overviews)

OpenAI’s public evals work (e.g. GDPval on economically valuable knowledge-work tasks) shows the field moving toward more realistic task suites—not only multiple-choice quizzes.

Everyday example

A practice SAT is useful; it is not the same as doing the job the degree claims to prepare you for.

Try it

Write 10 pass/fail items for one AI feature. Include 2 adversarial or weird cases described as *goals* (what must never happen), not attack steps.

Myths

⚠️ Myth: Top leaderboard = best product.
✓ Reality: Contamination, prompt formatting, mismatched use cases, and ignored latency/cost distort rankings (Anthropic’s MMLU caveats).
⚠️ Myth: LLMs can grade themselves perfectly.
✓ Reality: LLM-as-judge helps with caveats—calibrate against humans.
⚠️ Myth: One eval suite forever proves safety.
✓ Reality: New tools, prompts, and users create new failure modes—make evaluation continuous (NIST RMF / GenAI Profile spirit).

Sources