MICRO-COURSE

AI Evaluation & Reliability

AI Evaluation & Reliability: 12 short lessons, one for each concept. Evaluation datasets, reference answers, task-specific metrics, retrieval quality, factuality, groundedness, tool-call correctness, human evaluation, judge-model calibration, regression testing, adversarial testing, and quality–cos

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Evaluation datasets, reference answers, task-specific metrics, retrieval quality, factuality, groundedness, tool-call correctness, human evaluation, judge-model calibration, regression testing, adversarial testing, and quality–cost–latency trade-offs.

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12 results1–12

Lesson

Evaluation datasets

A short lesson on evaluation datasets from the AI Evaluation & Reliability micro course: what it is, where it shows up in real systems, and a practical example you can apply.

Open lesson

Lesson

Reference answers

A short lesson on reference answers from the AI Evaluation & Reliability micro course: what it is, where it shows up in real systems, and a practical example you can apply.

Open lesson

Lesson

Task-specific metrics

A short lesson on task-specific metrics from the AI Evaluation & Reliability micro course: what it is, where it shows up in real systems, and a practical example you can apply.

Open lesson

Lesson

Retrieval quality

A short lesson on retrieval quality from the AI Evaluation & Reliability micro course: what it is, where it shows up in real systems, and a practical example you can apply.

Open lesson

Lesson

Factuality

A short lesson on factuality from the AI Evaluation & Reliability micro course: what it is, where it shows up in real systems, and a practical example you can apply.

Open lesson

Lesson

Groundedness

A short lesson on groundedness from the AI Evaluation & Reliability micro course: what it is, where it shows up in real systems, and a practical example you can apply.

Open lesson

Lesson

Tool-call correctness

A short lesson on tool-call correctness from the AI Evaluation & Reliability micro course: what it is, where it shows up in real systems, and a practical example you can apply.

Open lesson

Lesson

Human evaluation

A short lesson on human evaluation from the AI Evaluation & Reliability micro course: what it is, where it shows up in real systems, and a practical example you can apply.

Open lesson

Lesson

Judge-model calibration

A short lesson on judge-model calibration from the AI Evaluation & Reliability micro course: what it is, where it shows up in real systems, and a practical example you can apply.

Open lesson

Lesson

Regression testing

A short lesson on regression testing from the AI Evaluation & Reliability micro course: what it is, where it shows up in real systems, and a practical example you can apply.

Open lesson

Lesson

Adversarial testing

A short lesson on adversarial testing from the AI Evaluation & Reliability micro course: what it is, where it shows up in real systems, and a practical example you can apply.

Open lesson

Lesson

Quality–cost–latency trade-offs

A short lesson on quality–cost–latency trade-offs from the AI Evaluation & Reliability micro course: what it is, where it shows up in real systems, and a practical example you can apply.

Open lesson

AI Evaluation & Reliability

Recorded video

Start with the topic guide.

Concept scope

Prepare a focused example

Check your understanding

Need a walkthrough?Stay with this topic.

Request a live session for this topic.

Explore 12 focused topics.

Evaluation datasets

Reference answers

Task-specific metrics

Retrieval quality

Factuality

Groundedness

Tool-call correctness

Human evaluation

Judge-model calibration

Regression testing

Adversarial testing

Quality–cost–latency trade-offs

Need a walkthrough?
Stay with this topic.