AI Testing Training in Hyderabad and Online Training
                     C O R P O R A T E  T R A I N I N G  S E R I E S
Generative AI Testing
Fundamentals and Best Practices for Beginners
Core concepts Test workflow Best practices Career paths
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Introduction: Why Generative 
AI Testing Matters Traditional testing
One fixed expected result
• Generative AI creates text, images, code, audio, and more.
• Outputs are probabilistic, so results can change between runs.
• Risks include hallucinations, bias, unsafe content, and data leaks. Generative AI testing
• Testing protects users, brands, and regulatory compliance.
A range of acceptable 
• Quality must be checked before release and continuously after. results
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Generative AI Testing
Core Concepts of 
Generative AI Testing
• Quality: is the output accurate and helpful?
D E F I N I T I O N • Safety: block harmful, toxic, or restricted content.
Generative AI testing checks • Fairness: avoid bias across groups and contexts.
whether AI-generated content 
is accurate, safe, fair, and • Robustness: handle vague, odd, or adversarial 
useful for its purpose. prompts.
• Grounding: answers stay faithful to trusted sources.
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Generative AI Testing
Key Components of a Testing Program
Test Scenarios Reference Data Evaluation Metrics
Realistic, edge-case, and risky Trusted answers and example Scores for quality, safety, bias, 
user requests outputs for comparison and groundedness
Human Review Guardrails Monitoring
Experts judge tone, nuance, Filters and policies that limit Track live quality, cost, and 
and real-world risk unsafe output behavior drift
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Generative AI Testing
How Generative AI Testing Works
1 2 3 4 5
Set Quality Design Test Run and Capture Evaluate Release and 
Goals Cases Outputs Monitor
Define success, Cover normal, edge, Execute prompts Use metrics, AI Gate releases, then 
risks, and and adversarial and record outputs judges, and human track live behavior
acceptance limits inputs and context review
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Generative AI Testing
Popular Tools and Technologies
Tool Purpose
Promptfoo Compare prompts and models using automated test cases
DeepEval Pytest-style evaluation with metrics for AI outputs
Ragas Evaluate quality of retrieval-augmented generation systems
LangSmith Trace, evaluate, and monitor AI application runs
Garak Probe models for security and safety weaknesses
Giskard Scan for bias, hallucination, and robustness issues
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Generative AI Testing
Real-World Use Cases
Customer Service Check assistants follow policy, tone, and escalation rules
Healthcare Verify summaries never invent clinical details
Banking and Finance Test for data leaks and compliant guidance
Marketing and Design Review copy and images for brand fit and bias
Software Engineering Validate generated code for security and correctness
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Generative AI Testing
Benefits and Challenges
Benefits Challenges
• Safer, more trustworthy AI releases • No single correct answer to compare 
against
• Lower risk of costly public failures
• Frequent model changes can break past 
• Faster, confident updates to prompts results
and models
• AI judges and metrics still need human 
checks
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Generative AI Testing
Career Opportunities and Future Trends
Job Roles Skills Needed
AI QA Engineer, AI Evaluation Python, prompt design, statistics 
Specialist, and Red Team Analyst basics, security awareness, and clear 
writing
Industry Demand Future Outlook
Rising as enterprise AI adoption and Agent testing, automated red 
regulation grow teaming, and continuous evaluation 
will expand
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Generative AI Testing
Summary and Conclusion
F I N A L  T H O U G H T
• Test quality, safety, fairness, and grounding together.
Trust in generative AI is 
• Use varied test data, including edge and risky cases. earned through consistent, 
measurable testing.
• Blend automated scoring with human judgment.
• Automate tests in pipelines and monitor live behavior.
• Treat testing as continuous, not a one-time task.
Next step: build a small test set for one AI 
feature this week.
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Generative AI Testing
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AI Testing
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Generative AI Testing
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Generative AI Testing