Test AI Chatbots & LLM Apps: A Practical Introduction for QA

AI Evaluation Foundations for QA Testers: A Practical Approach

AI assistants can produce responses that sound convincing but contain unsupported information, miss important details, or fail to follow documented requirements.

As a QA professional, how do you determine whether an AI-generated response is acceptable when there may be no single correct answer?

In this course, you'll learn the foundations of AI evaluation through practical examples using NexaDesk, a fictional internal service desk assistant.

Rather than jumping straight into automation, we'll first establish what needs to be evaluated, define expected behaviour, and explore practical ways to assess AI responses against documented requirements and clear evaluation criteria.

What Will You Learn?

• Design AI evaluation cases: Translate documented requirements into expected behaviour, required information, and prohibited claims.

• Identify requirement gaps: Recognise incomplete or ambiguous expectations instead of making assumptions when defining evaluation criteria.

• Understand evaluation methods: Explore rule-based checks, similarity measures, and rubric-based evaluation, including when each approach is appropriate.

• Build an evaluation dataset: Organise evaluation cases, inputs, expectations, and supporting information in a structured workbook.

• Evaluate AI responses: Review captured responses and record Pass, Fail, or Cannot Decide verdicts with evidence-based reasons.

• Prepare for automated evaluation: Understand why clear expectations, appropriate evaluation methods, and reviewed examples are essential before automation.

What Will You Work With?

Throughout the course, we'll use NexaDesk and a provided evaluation workbook.

You'll see how documented requirements become evaluation cases, how different methods help assess AI responses, and how to record evaluation decisions consistently.

The workbook provides a practical starting point you can adapt for your own AI evaluation activities.

Do You Need to Install Anything?

No installation or paid API access is required.

You can follow the demonstrations using captured AI responses and the provided workbook. You won't need to install NexaDesk, configure an API key, or make paid API calls.

Who Is This Course For?

This course is designed for:

• Manual testers and QA engineers who want to learn how to evaluate AI-generated responses.

• Automation testers who want to understand AI evaluation before moving into automated testing.

• Software testing professionals who want practical experience designing AI evaluation cases and datasets.

• Beginners in AI testing who prefer a hands-on introduction without coding.

Basic familiarity with software testing concepts is helpful. No previous experience with AI evaluation, machine learning, or large language models is required.

What Is Not Covered in This Course?

This is a foundational course focused on manual AI evaluation, not the implementation of an automated evaluation framework.

It does not teach Python programming, pytest automation, automated API evaluation, embeddings implementation, LLM-as-a-Judge coding, DeepEval, or CI/CD integration.

These topics belong to the next stage of learning: building and running an automated AI evaluation framework.

What Will You Have by the End?

By the end of this course, you'll have a structured starting point for evaluating AI responses using documented expectations, practical evaluation methods, and evidence-based decisions.

More importantly, you'll understand what needs to be evaluated and why before attempting to automate those evaluations.