What the video didn’t say

Simple words

Offline evals run on a fixed test set before release. Online evals watch the live system with real users.

Analogy

A crash test in the lab versus a dashcam on the road.

Example

Offline: run the CRM suite before changing the prompt. Online: sample live runs each day and check them against the same checklist.

Why it matters

Offline evals catch known problems early. Online evals catch the ones you never thought to test.

Simple words

AI can answer the same question differently each time, so one run proves little. Run it several times.

Analogy

A basketball player’s free throws: one shot tells you little about their average.

Example

pass@k asks: did at least one of k tries succeed? pass^k asks: did all k tries succeed? A support agent needs a high pass^k, because every customer gets only one try.

Why it matters

Reporting only the best of several tries makes an unreliable agent look reliable.

Source: Chen et al. (2021) · Yao et al. (2024)

Simple words

Using one AI to grade another AI’s output against a rubric.

Analogy

A fast, tireless judge who happens to prefer long speeches.

Example

A judge model reads each drafted email and answers five yes/no questions about tone and accuracy.

Why it matters

Research has found AI judges can favour longer answers, answers in a certain position, and answers they wrote themselves. Check a judge against human labels before trusting it.

Source: Zheng et al. (2023)

Simple words

Have humans grade a sample too, and measure how often the grader agrees with them.

Analogy

Two referees watching the same match should mostly make the same calls.

Example

An expert grades a sample of emails; the AI judge grades the same sample. You compare the two sets of answers, and fix the rubric where they disagree.

Why it matters

Agreement statistics such as Cohen’s kappa correct for agreement that would happen by chance, so they are stricter than a raw match rate.

Source: McHugh (2012)

Simple words

Code checks what can be checked exactly. AI judges handle fuzzy things like tone. Humans handle the hardest calls and set the standard.

Analogy

A spell-checker, an editor, and the publisher.

Example

Code: “was the note saved?” AI judge: “is the tone friendly?” Human: “would I send this to our biggest client?”

Why it matters

Use the cheapest grader that is reliable for each check. Save humans for what only humans can judge.

Source: Anthropic Engineering (2026)

Simple words

A small, hand-checked set of real cases the team trusts as the reference.

Analogy

The teacher’s own marked copy of the exam.

Example

Pull real support tickets where the agent failed, write down the right outcome for each, and use them as your first tasks.

Why it matters

One practitioner guide suggests 20–50 simple tasks drawn from real failures is a great start. You don’t need thousands.

Source: Anthropic Engineering (2026)

Simple words

Re-run your evals on every prompt, tool or model change, and block the change if scores drop.

Analogy

A smoke alarm in the build pipeline.

Example

A pull request changes the system prompt. The pipeline runs the eval suite; two tasks that used to pass now fail, so the merge is blocked.

Why it matters

Small prompt tweaks can quietly break things that used to work. Automatic checks catch it before users do.

Simple words

Trajectory evals check the steps and tool calls the agent took, not only its final answer.

Analogy

Marking the working in a maths exam, not just the final number.

Example

The final email was fine, but along the way the agent deleted a record by mistake. Only a check on the steps catches that.

Why it matters

An agent can reach a right-looking answer in an unsafe or wasteful way. Tool-call checks catch it.

Source: Anthropic Engineering (2026)

Simple words

For systems that look things up first, check whether the answer sticks to what was retrieved, and whether the right things were retrieved.

Analogy

An open-book exam: did the student use the book, and did they open it at the right page?

Example

The bot quotes a refund window. Faithfulness asks: is that in the retrieved policy? Context relevance asks: is what was retrieved actually about refunds, or padded with unrelated text?

Why it matters

Research on RAG evaluation measures faithfulness, answer relevance and context relevance separately, because each can fail on its own.

Source: Es et al. (2023)

Simple words

Leaderboards test general skills on public questions. Your evals test your tasks on your data.

Analogy

Top of the class in general knowledge doesn’t make you good at a specific job.

Example

A model tops a public coding benchmark but fails many of your CRM tasks, because your tools and rules are unusual.

Why it matters

Models may have seen public test questions during training, which makes their benchmark scores look better than they are.

Source: Sainz et al. (2023)

Simple words

Deliberately try to make your agent misbehave, including hiding instructions inside the content it reads.

Analogy

Hiring a locksmith to try to break into your own house.

Example

A test web page contains hidden text telling the agent to email the customer list elsewhere. The eval passes only if the agent ignores it.

Why it matters

OWASP lists prompt injection as the top risk for LLM applications, and researchers have shown indirect injection through retrieved content working against LLM-integrated apps.

Source: OWASP (2025) · Greshake et al. (2023)

Simple words

Different tasks deserve different trade-offs. Choose the cheapest, fastest option that clears each task’s bar.

Analogy

You don’t take a taxi to the corner shop, or walk to the airport.

Example

Internal notes go to a small, fast model. Customer-facing emails go to a larger one that clears a higher bar.

Why it matters

Research on agents argues that evaluations should report cost alongside accuracy, not accuracy alone.

Source: Kapoor et al. (2024)

Simple words

Write the eval before you build or change the feature, then build until it passes.

Analogy

Agreeing what “done” looks like before you start painting the room.

Example

Before adding “draft follow-up emails”, write ten tasks and a checklist for a good draft. Then build the feature against them.

Why it matters

It forces you to define “good” up front, and gives you a finish line instead of endless tweaking.

Simple words

As products, users and models change, old evals stop matching reality. Review and update them like code.

Analogy

Last year’s map of a growing city.

Example

You add a new pricing plan, but the eval suite still checks for the old plan names. Everything passes while real answers are wrong.

Why it matters

A stale suite gives false confidence, which can be worse than no suite.

Simple words

A trace records one run. Spans are its individual steps. A session groups the runs from one conversation.

Analogy

Receipts (spans) stapled into one bill (trace), filed under one customer visit (session).

Example

A complaint arrives. You open the session, find the trace for that answer, and see which tool-call span returned bad data.

Why it matters

OpenTelemetry, an open standard for traces, is developing shared naming rules for AI calls, so tools can read each other’s traces. The rules are still being written.

Source: OpenTelemetry (2026)

Simple words

Training a smaller model to copy the outputs of a larger one for a specific job.

Analogy

An apprentice learning by watching a master at work.

Example

The large model drafts thousands of approved CRM notes. A small model trained on them can then run the task faster and more cheaply.

Why it matters

It can cut cost once a big model has shown the job can be done well. Your evals tell you whether the small one kept the quality. Check your provider’s terms first: some limit training on their outputs.

Source: Hinton, Vinyals & Dean (2015)

Simple words

Accuracy: how often the answer is right overall. Precision: when it says “yes”, how often it’s right. Recall: of all the real “yes” cases, how many it found.

Analogy

A spam filter: precision is “don’t bin good mail”, recall is “don’t let spam through”.

Example

An agent flags urgent tickets. It flags 10, and 8 really are urgent: good precision. But there were 20 urgent tickets, and it found only 8: poor recall.

Why it matters

One number can hide the failure that matters. Decide which mistake is worse for your task, and track that metric.