AI & LLM Testing and Automation from Beginner to Master
– Live Training

• Traditional software vs AI-driven systems
• Deterministic vs probabilistic behavior in AI
• NLP basics for testers (tokens, context, embeddings)
• How LLMs generate responses (OpenAI, Azure OpenAI)
• Prompt structure, context windows, and response variability
• Why expected-output testing fails for AI systems
• Common AI failure patterns and hallucinations
• Bias, fairness, safety, and privacy risks in LLM outputs
• Regulatory awareness and Responsible AI fundamentals
• AI testing mindset and uncertainty-driven testing approaches

Tools / Platforms: OpenAI, Azure AI Foundry, Azure OpenAI Playground

• Differences between traditional and AI-focused test planning
• Defining AI quality goals and acceptance criteria
• Risk-based testing strategies for LLM applications
• Identifying high-impact AI failure scenarios
• Test scope and coverage decisions for AI features
• Cost-aware testing and token usage considerations
• AI test documentation and stakeholder communication
• Risk modeling for RAG systems (retrieval failure, hallucinated grounding)
• Agentic AI risk identification (looping, tool misuse, goal deviation)

Tools / Artifacts: AI test strategy templates, risk matrices, prompt catalogs

• Introduction to AI evaluation frameworks (DeepEval, prompt-based evaluators)
• Using DeepEval for automated evaluation (faithfulness, answer relevancy, context precision)
• LLM-as-a-judge evaluation techniques
• Groundedness validation for RAG outputs
• Evaluating hallucinations vs factual correctness
• Dataset-based vs dynamic evaluation approaches
• Behavior-based vs rule-based validation techniques
• Task completion and instruction-following checks
• Content quality validation (clarity, relevance, tone)
• Bias, fairness, and safety validation methods
• Performance validation (latency, consistency, response stability)
• Faithfulness, relevancy, and completeness metrics
• Robustness across prompt variations
• Toxicity, refusal, and safety-related metrics
• Custom scoring models and quality thresholds
• Release readiness assessment and quality trend analysis
• Tools / Techniques (Updated):
• Prompt-based validation
• Metric scoring models
• DeepEval framework
• Evaluation dashboards

Tools / Techniques: Prompt-based validation, Metric scoring models, DeepEval framework, Evaluation dashboards

• RAG test dataset creation (query + expected context + expected answer)
• Agent workflow prompt chaining and testing
• Test dataset design for multi-turn conversations and agents
• Prompts as first-class test assets
• Prompt design principles for reliability and testability
• Functional, bias, and safety prompt datasets
• Prompt versioning and change management
• Prompt regression testing strategies
• Impact analysis for prompt and model updates

Tools / Practices: JSON/YAML prompt datasets, GitHub version control

• Python essentials for AI testers
• Data structures for test inputs and outputs
• Reading and writing JSON and text data
• Calling LLM APIs (OpenAI, Azure OpenAI) and handling responses
• Exception handling, retries, and timeout logic
• Logging AI responses, errors, and latency
• Writing clean, maintainable automation utilities
• Calling evaluation frameworks (DeepEval APIs / libraries)
• Handling structured evaluation outputs (scores, reasoning)

Tools / Languages: Python, REST APIs, VS Code

• Architecture of AI test automation systems
• Automating prompt execution and evaluations
• Rule-based and metric-driven validations
• Handling non-deterministic and flaky AI tests
• Defining automated quality gates
• Generating structured test reports and summaries
• Integrating DeepEval into automation frameworks
• Automating RAG pipeline testing (retrieval + generation validation)
• Testing multi-step Agentic AI workflows
• Simulating user journeys for AI agents
• Validating tool usage and intermediate reasoning steps
• Performance/load testing of AI APIs
• Testing LLM endpoints under concurrent users
• Measuring latency under load
• 🔹 Playwright for End-to-End AI Testing of UI based AI apps
• Tools / Approaches (Updated):
• Custom Python frameworks
• Evaluation scripts
• DeepEval integration
• Reporting utilities

Tools / Approaches: Custom Python frameworks, Evaluation scripts, Reporting utilities, DeepEval integration

• Continuous testing concepts for AI systems
• Integrating AI tests into CI/CD pipelines
• Triggering tests on code, prompt, or model changes
• Quality gates using evaluation metrics
• GitHub Actions workflows for AI testing
• Jenkins overview (conceptual exposure)
• Managing flaky tests and execution costs
• Running DeepEval tests in CI/CD pipelines
• Automated quality gates based on evaluation scores
• Regression testing for RAG and agent workflows

Tools: GitHub Actions, Jenkins (conceptual)

• Pre-release testing vs post-release monitoring
• Observability concepts for AI systems
• Latency, failure, and safety telemetry
• Metrics collection using Prometheus
• AI quality dashboards using Grafana
• Detecting drift, degradation, and anomalies
• Alerts, feedback loops, and continuous improvement
• Monitoring RAG pipeline performance (retrieval accuracy, latency)
• Tracking agent behavior in production (failures, loops, incorrect actions)
• Observability for agentic workflows
• Logging intermediate steps in AI agents

Tools: Prometheus, Grafana

• Evaluating fine-tuned and customized LLM models
• Adversarial and red-team testing concepts
• Analysis of real-world AI failures
• Responsible AI practices in testing
• End-to-end AI quality engineering workflows
• AI testing roles and career paths
• Interview preparation and resume positioning
• RAG architecture testing (embeddings, vector DB, retrieval failures)
• Agentic AI testing strategies and challenges
• Failure case studies: RAG hallucinations, agent misbehavior
• End-to-end testing of AI systems (RAG + Agents + APIs)

Focus Areas: Real project scenarios, Career alignment, Agentic AI validation, RAG systems testing