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

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

3. Why is AI evaluation important for AI applications?

AI evaluation helps teams measure whether an AI system produces appropriate, reliable, accurate, and safe outputs against defined requirements. Structured testing can also help identify issues such as hallucinations, retrieval failures, inconsistent responses, unsafe outputs, and unreliable agent behavior.

4. Does the course cover RAG and AI agent evaluation?

Yes. The course includes dedicated modules for evaluating RAG systems and AI agents. Learners assess retrieval quality, context relevance, grounded responses, citations, task completion, tool usage, planning, multi-step workflows, and agent reliability.

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

Projects include creating an LLM evaluation dataset, developing an AI 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 concepts together.

AI Evals Course: Test and Evaluate AI Systems

Category Data Analytics & AI

Level Experts

Students 19 Already Enrolled

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(4.8)

An AI evaluation course teaches how to systematically test AI systems for quality, accuracy, reliability, safety, and performance. This course introduces evaluation frameworks for large language models, RAG applications, and AI agents, helping learners develop structured methods for measuring whether AI systems meet defined requirements.

Learners explore evaluation objectives, criteria, datasets, test cases, benchmarks, quality thresholds, factual accuracy, relevance, instruction following, consistency, and hallucination detection. The course also covers retrieval quality, grounded responses, source accuracy, agent task completion, tool usage, planning, and multi-step workflow testing.

Learners examine automated evaluation, human assessment, LLM-as-a-Judge methods, evaluation pipelines, safety testing, bias, privacy, prompt injection, and security risks. Through structured projects, they create evaluation datasets, test suites, and frameworks while analyzing results from LLM, RAG, and AI agent systems.

AI evaluation foundations, goals, quality dimensions, and challenges
Evaluation frameworks, datasets, test cases, benchmarks, and thresholds
LLM response quality, accuracy, relevance, consistency, and hallucinations
RAG retrieval, grounding, citations, source accuracy, and failure testing
AI agent task completion, planning, tool usage, and reliability
Automated evaluation, human assessment, AI safety, security, and evaluation projects

No formal AI evaluation qualification is required to enrol. Familiarity with artificial intelligence, LLMs, RAG systems, software testing, or AI application development can be helpful but is not essential.

This course is designed for AI engineers, developers, ML professionals, QA specialists, data professionals, researchers, and technical teams responsible for testing AI systems. It is also suitable for professionals building or managing LLM, RAG, and AI agent applications.

1.1 Understanding AI Evaluation
1.2 Why AI Evals Matter
1.3 AI System Evaluation Challenges
1.4 Types of AI Evaluation
1.5 Evaluation Goals and Requirements
1.6 AI Quality Dimensions

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

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

4.1 Testing Retrieval Quality
4.2 Measuring Context Relevance
4.3 Evaluating Grounded Responses
4.4 Testing Citation and Source Accuracy
4.5 Identifying Retrieval Failures
4.6 Optimizing RAG Evaluation

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

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

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 Assessing Security Vulnerabilities

8.1 Building an LLM Evaluation Dataset
8.2 Creating an AI 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 course is suitable for AI engineers, developers, QA professionals, ML practitioners, researchers, data professionals, and technical teams. It can also benefit professionals responsible for assessing the quality and reliability of generative AI applications.

AI Evals Course: Test and Evaluate AI Systems

About AI Evals Course: Test and Evaluate AI Systems

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