What will I learn?
Discuss the modules, practical projects, learning approach and upcoming course arrangements.
AI TESTING HUB / PRACTICAL TRAINING
AI Agent Testing
& Evaluation.
Develop the practical skills needed for an AI Engineer in Test role.
From AI fundamentals and no-code labs to golden datasets, Python evaluations, CI/CD and red teaming. Build a complete AI Agent Quality & Evaluation solution.
08Core modules
Hands-onNo-code labs + Python evals
RoleAI Engineer in Test
YOUR NEXT STEP IN QA
Get clarity.
Then get started.
Speak with an AI expert before deciding whether this course is right for you.
What does it cost?
Get the current course fee and ask about payment options before committing.
AI Engineer in Test
Prepare to test, evaluate and improve AI agents using practical quality engineering, automation, evaluation frameworks, CI/CD, security and governance skills. Employment is not guaranteed.
Is it right for me?
The course is designed for QA professionals, test engineers, automation engineers and test managers. Discuss your background, Python readiness and any relevant eligibility questions with an expert.
THE COURSE CURRICULUM
From fundamentals.
To practical assurance.
Concepts, no-code labs, Python evaluations and CI/CD. Explore each module below.
01AI Engineering & AI Agent Fundamentals
+
Understand LLMs, prompting, RAG, MCP, tools and agent architecture.
Hands-on outcome: Trace a request from the user through the model, knowledge sources and tools; create an annotated architecture diagram.
02No-Code AI Agent Development, Testing & Evaluation
+
Build agents with platforms such as n8n, connect tools and knowledge, and evaluate responses and tool usage.
Hands-on outcome: Build a no-code agent and run at least 10 test cases, recording outputs, pass/fail decisions and metric scores.
03AI Evals & Evaluation Metrics
+
Explore correctness, relevance, helpfulness, groundedness and safety, plus reference-based and reference-free evaluation.
Hands-on outcome: Create an evaluation scorecard and compare good and poor responses using multiple quality dimensions.
04Golden Dataset Preparation & AI-Generated Test Data
+
Design human-reviewed reference cases, expand coverage with synthetic data, and manage tagging, deduplication and versioning.
Hands-on outcome: Create 10 curated golden cases and 20–30 AI-generated cases, then review and organise the dataset.
05AI Agent Evaluation Lifecycle
+
Combine assertions, LLM-as-a-Judge, live experiments, trajectory testing, red teaming and production feedback.
Hands-on outcome: Map checks across pull requests, nightly runs, pre-release testing and production.
06Python AI Evaluation Frameworks
+
Use DeepEval, LLMTestCase, GEval and tool-call metrics, with custom rubrics and evaluation reporting.
Hands-on outcome: Build a Python evaluation framework and run it against at least 10 agent outputs.
07AI Regression Testing & CI/CD
+
Connect golden datasets, pytest, quality gates, baseline comparisons and red-team stages to delivery pipelines.
Hands-on outcome: Create pull-request gates, nightly evaluations and pre-release red-team stages in a sample pipeline.
08AI Governance, Risk & Compliance
+
Explore privacy, bias, security, human oversight and audit evidence, with an overview of GDPR, the EU AI Act, UK regulation, NIST AI RMF and ISO/IEC 42001.
Hands-on outcome: Build a governance testing checklist, AI risk register and risk/test/evidence matrix.
OPTIONAL TRACK
Using AI in Software Testing
- Using AI to generate testing artifacts
- Developing test cases and test data using AI
- AI-assisted automation with JavaScript and Playwright
THE FINAL COURSE PROJECT
Build the evidence.
Show your skills.
Build or use an AI agent and create a complete quality and evaluation solution that brings together the course’s practical work.
Talk to an AI expertYOUR PROJECT INCLUDES
AI agent + golden dataset
No-code metrics + LLM-as-a-Judge
Python / DeepEval framework
Tool-call, trajectory + regression testing
CI/CD + red-team testing
Governance and compliance evidence
ENROLMENT ENQUIRY
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with a conversation.
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