Build practical skills in LLM test automation course concepts and learn how to test and evaluate Generative AI applications, LLMs, RAG systems, and AI agents. Designed for automation engineers, SDETs, and API testers, Quality Thought helps experienced QA professionals apply their automation and testing skills to modern AI systems.
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Everything you need to know before enrolling in Gen AI Testing our program
Comprehensive modules covering AI testing, LLM evaluation, security, RAG, AI agents, automation, and practical Gen AI testing workflows. Explore our courses to discover more learning opportunities.
● Difference between traditional software and AI systems
● AI application architecture: Model → Prompt → App → UI
● Probabilistic outputs vs deterministic validation
● AI defect categories overview
● AI testing lifecycle and evaluation approach
● Tokens, context window, and cost
● Temperature and randomness control
● Prompt engineering basics
● RAG vs Fine-tuning
● LLM inference pipeline
● Hallucination detection
● Prompt injection attacks
● Bias and fairness
● Inconsistent behavior
● AI bug reporting
● AI test case format
● Evaluation criteria design
● Statistical testing
● Boundary and edge cases
● Test library creation
● Oracle design
● Golden datasets
● Open vs closed domain testing
● Rate measurement
● Reporting
● OWASP LLM Top 10
● Injection testing
● Jailbreaking
● RAG poisoning
● Security reporting
● Bias taxonomy
● Counterfactual testing
● Statistical validation
● Consistency testing
● Compliance
● RAG architecture testing
● Context validation
● RAGAS metrics
● Negative testing
● Source attribution
● Agent architecture
● Tool testing
● Memory testing
● Multi-agent validation
● Trace debugging
● Threat modeling
● Jailbreak testing
● Adversarial testing
● Toxicity testing
● Report writing
● Python basics
● API testing
● Async testing
● Environment management
● Error handling
● Test structure
● Fixtures
● Mocking
● Reporting
● Test pyramid
● Evaluation metrics
● Custom metrics
● Dataset creation
● Integration
● CI gating
● Pipeline setup
● Metrics
● Dataset creation
● Regression testing
● CI integration
● Versioning
● Regression testing
● A/B testing
● Adversarial testing
● Optimization
● Framework setup
● Unit testing
● State testing
● Trace assertions
● Regression testing
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Updated syllabus covering latest technologies and industry best practices with hands-on projects
Not just training, but complete career transformation with soft skills and interview preparation
Multiple batch timings to suit working professionals, students, and freshers
Work on real client projects during internship at Ramana Soft IT company
Flexible payment options including pay after placement for eligible candidates
Quality Thought helps experienced QA professionals build practical skills for evolving AI testing environments. The program supports professionals looking to strengthen their career path through practical training, including opportunities related to the gen ai Testing internship course and the gen ai Testing job guarantee course.
Get industry-recognized certification that validates your skills and boosts your career prospects.Professionals building AI testing skills can also explore is there a generative AI testing certification to understand the role of certification alongside practical testing and automation experience.
AI Powered Testing Program
This certifies that
[Your Name]
has successfully completed the
AI Powered Testing Program
Training Program
Ameerpet, Hyderabad
QT-2024-XXXX
Our certificates are recognized by leading companies and add credibility to your resume
Proves your expertise in AI Powered Testing in all mentioned technologies
Increases your chances of getting hired and helps in salary negotiations
Get both digital certificate for online sharing and physical certificate for framing
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For major projects completed during training
Find answers to common questions about Gen AI testing & training program
DeepEval is an open-source Python framework for evaluating LLM applications in a unit-test style, providing built-in and custom metrics, dataset-level evaluation, red-teaming support and CI integration through a pytest-like workflow.
RAGAS is a Python library focused on evaluating retrieval-augmented generation pipelines, with metrics such as faithfulness, answer relevancy and context precision and recall. DeepEval is broader and more test-suite oriented, making them complementary for different evaluation needs. Organisations building structured LLM evaluation capabilities can also explore LLM evaluation corporate training.
Use both for a RAG system: RAGAS can diagnose whether retrieval or generation is the weak link, and DeepEval can run the resulting checks as a maintained test suite in CI. Professionals comparing different AI testing learning options can also explore the best AI testing course 2026.
The framework knowledge transfers less than you might expect, but the discipline transfers completely: test structuring, fixtures, CI pipelines, failure management and reporting are the same problems in a new domain. Professionals moving from automation testing can also explore automation tester to AI testing skills.
Directly and unusually well. Testing whether an agent selects the right tool, passes valid parameters and handles errors is contract testing, which API testers already do. Practical exposure can be developed through gen ai Testing real-time project work.
Yes, and the course covers it, including the practical constraints: evaluation calls cost money and take time, so suites are usually tiered with a fast subset on every commit and a fuller run nightly or pre-release. Professionals looking for an intensive learning format can also explore the gen ai Testing bootcamp course.
You stop treating variation as a flake and set thresholds instead: score a response on defined metrics and pass or fail against a threshold agreed with the team, then track score distributions over time rather than individual runs. This is especially relevant as QA responsibilities evolve alongside AI and the question of will AI replace software testers becomes more important.
It involves testing tool selection and parameters, multi-turn conversation state, task completion and the path taken to it, including failure modes such as loops and wrong-tool calls. These skills can support professionals exploring AI QA engineer jobs USA and other AI-focused QA roles.
Batch schedules depend on the available training schedule. Learners who specifically prefer classroom-based learning can also explore generative AI testing training in Hyderabad.
Yes, including injection and jailbreak techniques, adversarial prompt design, PII leakage checks, toxicity and bias testing, using red-teaming tooling alongside manual adversarial design.
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