Mastering AI and Large Language Model (LLM) Testing

Mastering AI and Large Language Model (LLM) Testing

(Master end-to-end testing, evaluation, and automation for AI and Large Language Models. Build the skills to ensure accuracy, fairness, and reliability in intelligent systems)

 

This course prepares learners to become experts in quality assurance and testing for AI and Large Language Model (LLM) applications. Starting from fundamental AI concepts, learners will master various test types, evaluation metrics, modern tools, and automation frameworks, culminating in the ability to design, implement, and maintain comprehensive AI/LLM testing pipelines.

 

About the Instructor:

With over 12 years of rich IT industry experience and 4+ years of expertise in corporate and online training, Praveen is a seasoned QA professional specializing in both manual and automation testing for web, mobile, and API applications. An expert in modern automation tools such as Playwright, Selenium, Cypress, Appium, and Rest Assured, he has successfully trained numerous teams to build scalable and efficient test automation frameworks. In recent years, Praveen has expanded expertise into AI and LLM testing, focusing on evaluation metrics, safety testing, and automation pipelines for intelligent applications. Passionate about upskilling professionals in next-generation QA, he brings a hands-on, practical approach that bridges traditional testing with modern AI-driven quality assurance practices.

 

Live Sessions  Price:

For LIVE sessions – Offer price after discount is 300 USD 259 USD 229 USD Or 13000 INR 12900 INR 19900 Rupees

Enroll For Free Demo

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What will I learn by the end of this course?

By the end of this course, learners will be able to:

• Explain key AI and LLM concepts and architectures
• Identify unique challenges in AI/LLM testing
• Perform functional, bias, safety, performance, and usability testing for AI systems
• Use popular automation frameworks like Playwright, DeepEval, RAGAs, LangSmith, OpenAI Evals, and Promptfoo
• Develop automated test suites with comprehensive evaluation metrics
• Integrate AI testing into CI/CD workflows for continuous quality assurance
• Evaluate AI model outputs for fairness, toxicity, and accuracy
• Build trustable, safe, and user-friendly AI applications

 

Free Demo Session:

10th November @ 8:00 PM – 9 PM (IST) (Indian Timings)

10th November @ 9:30 AM – 10:30 AM (EST) (U.S Timings)

10th November @ 2:30 PM – 3:30 PM (BST) (UK Timings)

 

Class Schedule:

For Participants in India: Monday to Friday @ 8:00 PM – 9:00 PM (IST)

For Participants in the US: Monday to Friday @ 9:30 AM – 10:30 AM (EST)

For Participants in the UK: Monday to Friday @ 2:30 PM – 3:30 PM (BST)

 

The trainer will be on planned leave on 28th November 2025, 1st and 2nd December 2025

 

What students have to say about Chandu:

👩 Reshma Begum I definitely recommend Praveen. His teaching was so amazing. 👩 Athiya Mohammed Praveen’s sessions made AI and LLM testing so easy to understand. His real-world examples and hands-on exercises helped me confidently apply testing techniques to real projects. 👨 Vamshi Krishna Before joining, I struggled to understand evaluation metrics and automation tools. Praveen’s clear explanations and practical demos made AI testing concepts crystal clear — now I can design test pipelines independently. 👩 Snehal Mehta Best AI Testing instructor I’ve learned from! Praveen makes complex ideas feel simple and approachable. 👩 Deepika Iyer An amazing learning experience! Praveen connects every topic to real-world LLM use cases and ensures everyone gains practical confidence. 👨 Aman Tiwari What stood out was Praveen’s depth of knowledge and patience. His teaching style combines technical clarity with practical insights that make AI and LLM testing truly engaging. 👨 Vikash Rao Praveen’s sessions are structured, interactive, and full of practical knowledge. He bridges the gap between traditional QA and modern AI-driven testing. Highly recommend his training to anyone serious about advancing in AI quality assurance.

 

Salient Features:

• 50+ Hours of Live Training along with recorded videos
• Lifetime access to the recorded videos
• Course Completion Certificate

 

Who can enroll for this course?

• Beginners in software testing interested in AI quality assurance
• QA professionals transitioning to AI/LLM domains
• Developers and data scientists seeking test automation techniques
• AI product managers and tech leads focused on model reliability and safety

 

Course syllabus:

Module 0: Introduction to AI and LLMs

• Introduction to Python and installation setup
• Understanding variables, data types, and operators
• Control flow: conditional statements and loops
• Working with functions and reusable modules
• Lists, tuples, sets, and dictionaries in test data handling
• String manipulation and regular expressions
• File handling and JSON data parsing
• Exception handling, debugging, and logging best practices
• Virtual environments and dependency management using venv and pip
• Writing clean, modular, and maintainable Python scripts
• Mini hands-on exercises for automating small QA tasks

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Module 1: Introduction to AI and LLMs

• What is Artificial Intelligence?
• Evolution and current trends in AI
• Foundations of Natural Language Processing (NLP)
• Introduction to Large Language Models (GPT, PaLM, LLaMA)
• Real-world applications and case studies of LLM-powered apps

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Module 2: Unique Testing Challenges in AI/LLM

• Differences between traditional software and AI systems testing
• Probabilistic, non-deterministic outputs
• Risks: hallucinations, bias, toxicity, privacy concerns
• Ethical and legal implications
• Regulatory landscape and compliance basics

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Module 3: Types of Testing in AI/LLM Applications

• Functional testing principles in AI contexts
• Bias and fairness testing: detecting and mitigating unwanted bias
• Safety and ethical testing: toxicity, refusal, harmful content prevention
• Performance and scalability testing for AI inference services
• Usability and accessibility testing for AI user interfaces
• Advanced types: explainability, regression, localization, logging/auditing, disaster recovery

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Module 4: Evaluation Metrics for AI/LLM Outputs

• Understanding evaluation metrics: what to measure and why
• Core metrics: faithfulness, relevancy, completeness
• Fairness, bias scores, stereotype detection
• Safety metrics: toxicity, refusal rate
• Robustness and consistency
• Latency and scalability measures
• Sentiment, readability, coherence, and fluency
• Privacy and compliance metrics

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Module 5: AI/LLM Testing Frameworks and Tools

• Overview of popular testing frameworks
• Playwright for frontend UI automation
• DeepEval for multi-metric LLM evaluation
• RAGAs for retrieval-augmented generation pipelines
• LangSmith for monitoring and evaluation dashboards
• OpenAI Evals for configurable end-to-end testing
• Promptfoo for prompt-output assertions

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Module 6: Hands-On Setup and Test Automation

• Environment setup: Node.js, Python virtualenv, API keys
• Writing and running Playwright UI tests
• Implementing DeepEval Python test scripts
• Creating RAGAs evaluation pipelines
• Using LangSmith SDK and dashboards
• Building OpenAI Evals configs and running CLI tests
• Defining Promptfoo YAML test scenarios
• Automating test suites for continuous integration

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Module 7: Designing Test Suites and Datasets

• Curating effective test inputs and output references
• Creating diverse prompt sets for bias and safety analysis
• Building benchmark datasets for regression and model updates
• Incorporating edge case and adversarial inputs
• Dataset versioning and maintenance best practices

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Module 8: Integrating AI/LLM Testing into CI/CD Pipelines

• Overview of CI/CD concepts for AI apps
• Connecting tests to GitHub Actions, Jenkins, or other platforms
• Automating evaluations on model updates or frontend changes
• Monitoring test results and alerting mechanisms
• Managing flaky tests and false positives

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Module 9: Fine-Tuning and Red Teaming in AI Systems

• Understanding model fine-tuning: purpose and benefits
• When to fine-tune vs. use pre-trained models
• Types of fine-tuning: instruction tuning, domain adaptation, RLHF
• Steps in fine-tuning: data preparation, labeling, and evaluation
• Testing fine-tuned models for performance, bias, and hallucination reduction
• Continuous evaluation and version tracking after fine-tuning
• Concept and importance of red teaming in AI testing
• Adversarial prompt testing and jailbreak scenarios
• Safety, compliance, and ethical vulnerability assessments
• Detecting bias, toxicity, and harmful content generation
• Simulating attacks for privacy and data leakage
• Red teaming tools and frameworks overview
• Integrating red team results into QA pipelines
• Real-world examples and lessons learned from red teaming exercises

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Module 10: Case Studies and Industry Practices

• Analysis of real AI system failures and lessons learned
• Successful AI testing implementations
• Ethical AI and responsible innovation in testing context
• Future trends: multimodal AI, self-supervised evaluation

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Module 11: Capstone Project and Assessment

• Learners design and implement a full testing pipeline
• Test automation covering UI, backend, and model outputs
• Evaluate a publicly available LLM or AI chatbot system
• Submit report and demo with lessons learned
• Peer review and instructor feedback

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How can I enroll for this course?

Enroll For Free Demo

OR

For any other details, Call me or Whatsapp me on +91-9133190573

 

Live Sessions  Price:

For LIVE sessions – Offer price after discount is 300 USD 259 USD 229 USD Or 13000 INR 12900 INR 19900 Rupees

 

Sample Course Completion Certificate:

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Testimonials:

Important Note:
To maintain the quality of our training and ensure smooth progress for all learners, we do not allow batch repetition or switching between courses. Once you enroll in a batch, please make sure to attend the classes regularly as per the schedule. We kindly request you to plan your learning accordingly. Thank you for your support and understanding.