2. What skills will I gain from AI evaluation and testing training?

You can develop skills in AI test framework design, dataset creation, evaluation criteria, benchmarking, LLM testing, RAG evaluation, and AI agent testing. The course also covers automated evaluation, human assessment, LLM-as-a-Judge methods, safety testing, and security evaluation.

3. Why is AI testing important?

AI systems can produce inaccurate, inconsistent, irrelevant, or unsafe outputs, making structured testing an important part of development and deployment. Evaluation helps teams identify performance issues and compare system outputs against defined quality requirements.

4. Does the course cover LLM, RAG, and AI agent testing?

Yes. Dedicated modules address all three areas. Learners evaluate LLM response quality, RAG retrieval and grounding, and AI agent planning, tool usage, task completion, multi-step workflows, and reliability.

5. What projects are included in the AI evaluation and testing course?

Projects include building an AI test dataset, creating an LLM test suite, evaluating a RAG application, testing an AI agent, and analyzing evaluation results. Learners also build an AI evaluation framework that brings the course methods together.

AI Evaluation and Testing Course

Category Data Analytics & AI

Level Experts

Students 19 Already Enrolled

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(4.7)

AI evaluation and testing provides a structured approach to assessing whether artificial intelligence systems deliver accurate, relevant, reliable, and safe results. This course teaches learners how to design evaluation frameworks and test LLM applications, RAG systems, and AI agents against defined quality standards.

Learners explore evaluation objectives, criteria, datasets, test cases, benchmarks, quality thresholds, factual accuracy, relevance, instruction following, consistency, and hallucination detection. The curriculum also covers retrieval quality, context relevance, source attribution, agent task completion, planning, tool usage, multi-step workflows, and reliability.

The course examines automated evaluation, human assessment, LLM-as-a-Judge methods, evaluation pipelines, safety testing, bias, privacy, prompt injection defenses, and security vulnerabilities. Through structured projects, learners create AI test datasets and test suites, evaluate LLM, RAG, and agent applications, analyze results, and build evaluation frameworks.

AI evaluation foundations, quality dimensions, standards, and challenges
AI test frameworks, datasets, criteria, benchmarks, and test cases
LLM testing for accuracy, relevance, consistency, and hallucinations
RAG testing for retrieval, context, grounding, and source attribution
AI agent testing for planning, tools, workflows, and reliability
Automated evaluation, human testing, safety, security, and evaluation projects

No formal AI testing qualification is required to enrol. Familiarity with artificial intelligence, LLMs, RAG applications, AI agents, software testing, or quality assurance can be helpful but is not essential.

This course is designed for AI engineers, software developers, QA professionals, ML practitioners, data professionals, researchers, and technical teams responsible for evaluating AI systems. It is also suitable for professionals who want structured methods for testing generative AI applications.

1.1 Understanding AI Evaluation
1.2 Why AI Testing Matters
1.3 AI Quality Dimensions
1.4 Types of AI Evaluations
1.5 Evaluation Challenges and Limitations
1.6 Defining AI Quality Standards

2.1 Defining Evaluation Objectives
2.2 Creating Evaluation Criteria
2.3 Building Test Datasets
2.4 Designing AI Test Cases
2.5 Creating Evaluation Benchmarks
2.6 Establishing Quality Thresholds

3.1 Evaluating LLM Response Quality
3.2 Measuring Factual Accuracy
3.3 Testing Relevance and Helpfulness
3.4 Evaluating Instruction Following
3.5 Measuring Response Consistency
3.6 Detecting AI Hallucinations

4.1 Evaluating Retrieval Quality
4.2 Testing Context Relevance
4.3 Measuring Grounded Responses
4.4 Evaluating Source Attribution
4.5 Identifying Retrieval Failures
4.6 Improving RAG Performance

5.1 Evaluating Agent Task Completion
5.2 Testing Agent Planning
5.3 Evaluating Tool Usage
5.4 Testing Multi-Step Workflows
5.5 Measuring Agent Reliability
5.6 Monitoring Agent Behavior

6.1 Automated AI Evaluation
6.2 Human Evaluation Techniques
6.3 LLM-as-a-Judge Methods
6.4 Building Evaluation Pipelines
6.5 Comparing Model Outputs
6.6 Managing Evaluation Results

7.1 Testing AI Safety
7.2 Evaluating Bias and Fairness
7.3 Testing Harmful Outputs
7.4 Evaluating Privacy Risks
7.5 Testing Prompt Injection Defenses
7.6 Identifying AI Security Vulnerabilities

8.1 Building an AI Test Dataset
8.2 Creating an LLM Test Suite
8.3 Evaluating a RAG Application
8.4 Testing an AI Agent
8.5 Analyzing Evaluation Results
8.6 Building an AI Evaluation Framework

An AI evaluation and testing course is suitable for AI engineers, developers, QA specialists, ML professionals, researchers, data professionals, and technical teams. It can also benefit professionals responsible for assessing the quality, reliability, and safety of AI applications.

AI Evaluation and Testing Course

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