LLM Testing and AI Model Validation
Course Overview
Key Points
Design test strategies for LLM-powered applications
Build prompt testing frameworks and test suites
Detect and measure AI hallucinations systematically
Evaluate model bias, fairness, and safety
Create automated LLM evaluation pipelines
Test RAG systems and AI agents end-to-end
Apply red-teaming techniques to AI systems
Deliver an AI model testing audit as capstone
Course Outline
Module 1: LLM Fundamentals for Testers
- How LLMs work — what testers need to know
- Types of AI failures: hallucination, bias, drift
- Testing vocabulary: recall, precision, groundedness
- Setting up LLM testing environments
Module 2: Prompt Testing and Validation
- Prompt engineering from a testing perspective
- Boundary testing and adversarial prompts
- Output structure validation and schema checks
- Consistency and determinism testing
Module 3: Hallucination and Bias Detection
- Hallucination types and detection strategies
- Factual accuracy evaluation techniques
- Bias testing frameworks and fairness metrics
- Building automated hallucination test suites
Module 4: RAG and Agent Testing
- Testing retrieval-augmented generation pipelines
- Evaluating context relevance and answer quality
- Multi-step AI agent testing strategies
- Tool use and function calling validation
Module 5: Evaluation Pipelines and Automation
- Building LLM evaluation pipelines with Python
- Using frameworks like RAGAS, DeepEval, and Promptfoo
- Regression testing for AI model updates
- CI/CD integration for continuous AI evaluation
Module 6: Capstone — LLM Testing Audit
- Full test audit of a real LLM application
- Red-teaming session and safety evaluation
- Reporting findings and recommendations
- Career paths in AI testing and model evaluation
Ahsan Akhtar
Senior Test Automation Engineer & AI Trainer
This Course Includes
Duration 6-8 weeks (Weekend Classes Only)
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