AI Quality Engineering
AI Testing & Validation Engineering Professional
Build, Validate and Automate AI Quality Systems
7 Modules + Capstone
~80% Applied
Integrated GitHub Portfolio
6 Weeks
Why It Matters
Traditional QA
Manual flows, deterministic results
→
Automation
Repeatable regression frameworks
→
API Testing
Contracts, schemas, integration
→
AI Response Testing
Non-deterministic outputs, quality thresholds
→
LLM Validation
Grounding, robustness, safety, regression
→
Agent Testing
Plans, tools, state, loops, stopping
→
AI Quality Engineering
Evaluation harnesses, automated quality gates
What Modern AI Systems Need From Quality Engineering
LLM Applications
Prompts, outputs, formats and factual quality
RAG Systems
Retrieval, grounding, context and citation quality
AI Agents
Planning, tool use, memory, loops and stop conditions
AI Workflows
APIs, data, models, controls and human hand-offs
Common AI Failure Classes
Hallucination & weak grounding
Unsafe or biased output
Incorrect tool selection
Incorrect tool parameters
Infinite loops
Failed stopping logic
Schema failures
Latency & integration failures
Quality drift after prompt changes
Quality drift after model changes
Who Should Attend
QA Engineers moving into AI quality engineering
Automation Engineers
SDETs
Engineers who want a portfolio-driven, applied learning path
Learning Journey
MODULE 0
Environment & AI Testing Foundations
MODULE 1
Python for Test Automation
MODULE 2
Pytest Engineering
MODULE 3
AI Response Validation
MODULE 4
Bias, Robustness & Safety
MODULE 5
AI Agent Testing
MODULE 6
CI/CD for AI Testing
CAPSTONE
Integrated AI Testing Framework
Curriculum
What You’ll Achieve
Build
Python and Pytest automation foundations
Validate
APIs, schemas and AI responses
Evaluate
Grounding, robustness, safety and regression
Test Agents
Plans, tools, loops, state and stop conditions
Automate
Run AI quality checks through CI/CD
Demonstrate
Present one integrated GitHub portfolio
AI Agent Testing Workflow
Goal
→
Plan
→
Act / Tool
→
Observe
→
Stop or Repeat
Repeat happens only when the stop condition is not met — unmet stop conditions are exactly where infinite loops and runaway agents come from.
Planning validation
Tool selection
Parameter validation
Tool mocks
State & memory
Loop detection
Stop conditions
Real-model integration
From Local Execution to Automated Quality Gates
Code Change
→
Pytest Suite
→
AI Quality Checks
→
Reports
→
GitHub Actions
→
Merge Decision
The program moves testing from local execution into automated quality gates, not one-off script runs.
Technology Ecosystem
Programming
Python
Testing
Pytest
Integration
REST APIs
JSON / JSON Schema
Engineering Workflow
Git · GitHub
VS Code Agent
AI Layer
Mock LLMs · Real LLMs
CI/CD
GitHub Actions
Evidence
HTML Reports
JSON Reports
Capstone / Portfolio
ai-testing-framework/
├── app/
│ ├── validators/
│ └── agent/
├── tests/
│ ├── test_api.py
│ ├── test_ai_response.py
│ ├── test_safety.py
│ └── test_agent.py
├── datasets/
├── prompts/
├── reports/
├── .github/workflows/
├── .env.example
├── requirements.txt
└── README.md
Portfolio Evidence
✓
Python + Pytest foundation
✓
API and schema validation
✓
LLM response testing
✓
Regression testing
✓
Bias and safety scenarios
✓
Agent testing
✓
Loop and stop-condition testing
✓
Automated reports
✓
GitHub Actions workflow
✓
Technical README
One evolving repository, not disconnected mini-projects.
Career Relevance
AI QA Engineer
GenAI Quality Engineer
LLM Validation Engineer
Agent Testing Engineer
Agent Validation Engineer
The program develops demonstrable applied skills and portfolio evidence. Career outcomes still depend on prior experience, practice depth, communication and employer requirements.
Frequently Asked Questions
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