AI Fundamentals: AI Testing, GenAI & Prompt Engineering

Learn how artificial intelligence teaches computers to learn from data, understand questions, and make human-like decisions, improving over time by learning patterns instead of fixed rules.

Explore how ai differs from rule-based systems using spam detection as a simple example; ai learns from data and patterns to improve accuracy, unlike fixed rules.

Explore machine learning as a subset of AI that teaches a computer to learn from past data and recognize patterns, making decisions without fixed rules.

Explore the four types of machine learning—supervised, unsupervised, semi-supervised, and reinforcement learning. See how supervised learning uses data with the correct answer, like email spam detection.

Explore the four types of machine learning—supervised, unsupervised, semi supervised, and reinforcement learning—and understand how they shape testing, risk, and real-world examples like spam detection, customer grouping, and photo labeling.

Explore supervised, unsupervised, semi-supervised, and reinforcement learning and how each learns—from answer to few answers to trial and error—with examples like spam detection, customer grouping, and photo labeling.

Explore how deep learning learns layer by layer, from lines and edges to the overall face shape. See how the computer recognizes a new face after learning from many examples.

Discover how gen ai creates new content, from generating emails in varied tones to producing images and stories based on learned data.

Explore AI, ML, DL, and Zen AI basics, showing how AI makes systems smart, ML learns from data, and DL uses deep layers for pattern recognition and generative AI content.

Explore large language models, or LLMs, trained on vast text to read and generate text by predicting the next word, noting they are not applications.

Explore what AI software is, from chatGPT to Gemini, and how it uses an LLM behind a UI with guardrails and safety rules.

Define AI agent as an LLM-powered system that plans, acts, and automates tasks beyond chat functionality. Learn to test agents for control, permissions, and safe rollback using flight-booking examples.

Explore AI copilot as an AI agent with human in the loop, where AI suggests code and humans review and approve or reject, ensuring human oversight in AI-assisted development.

Understand retrieval augmented generation (rag) and why RAG failures in production stem from AI limitations, not LLM limits; retrieve internal documents to guide generation toward correct, confidential information.

Explore how large language models generate text, how retrieval-augmented generation uses trusted data for internal queries, and how ai agents and co-pilots plan and act.

Understand the differences among automated workflow, AI workflow, and agentic workflow, showing how rules-based systems differ from AI that can plan, act, and retry, with goal-driven testing and guardrails.

Learn the high-level ai system architecture—from user input and ui layer to ai app logic and rag data retrieval—securing safety checks, llm processing, and guardrails to deliver a polished response.

Understand single versus multi-agent architectures where an AI agent thinks, acts, uses tools, and stops at a goal; use single agents for simple tasks and multi-agent teams for complex tasks.

Standardize AI model connections with MCP, the model context protocol, by providing an interface for tools and data—a middle layer like usb-c that enables access to databases, files, and APIs.

LangChain acts as a glue that connects LLMs, tools, logic, and steps, enabling you to build real AI applications with LangGraph workflow.

Explore how AI testing differs from traditional rule-based testing, focusing on probabilistic outputs and data-driven decisions. Assess behavior over fixed outputs with examples like spam filtering and fraud detection.

Machine learning, a subset of AI, learns from data and improves over time, producing outputs that can change after retraining, guiding more evaluation than fixed testing.

Examine why traditional testing fails for AI systems as they learn from data, evolve behavior, and produce probabilistic outputs with dynamic data, unlike fixed test cases.

Recognize that automation alone cannot ensure AI quality as exact matches fail when outputs vary; evaluate output reasonableness, safety, bias, and confidence rather than fixed results.

Explore how AI systems differ from traditional rule-based logic and how input, model, and data drive outputs. Learn how data quality and prompt engineering shape AI behavior.

Understand how data drives AI systems, why data quality matters, and how continuous training, biased data risks, and testing behavior, not code, shapes AI outputs.

Explore the main ai testing types: data, model, functional, non-functional, and ethical, and see how data quality drives reliable decisions in applications like loan approvals and fraud detection.

Explore functional testing in AI by validating responses to valid, invalid, and boundary inputs, including empty, long, and special characters, focusing on behavior over fixed outputs.

Explore non-functional testing for AI systems by examining performance, scalability, and reliability under increasing load, using a chatbot example to illustrate response time and accuracy under stress.

Explore ethical and bias testing in ai, focusing on fairness and inclusivity. See how gender, college, or name can affect outputs like resume screening or trip-planning suggestions.

Learn security and privacy testing for ai systems, ensuring chatbots do not reveal internal system details or internal data, and guard against prompt abuse in gen ai.

Monitor AI behavior in production and post production testing, and continuously retrain models, testing intelligence quality beyond UI or API, and apply data and performance testing, ethics, and behavior comparison.

Learn variation testing to verify AI intent recognition by feeding multiple inputs for the same goal, such as password reset, and spot inconsistencies as bugs.

Explore boundary and edge case testing for ai systems, checking responses to empty or malformed inputs. Ensure graceful handling, clear guidance, and no crashes or hallucinations through practical test cases.

Practice negative and abuse testing to ensure AI refuses harmful prompts safely, politely, and consistently, with examples like invalid input, abusive language, and safe redirection to resources.

Explore hands-on consistency testing for AI to ensure a stable core meaning and guidance across repeated inputs. Accept wording or tone changes, but avoid contradictory results and flip-flops in recommendations.

Learn bias and fairness testing in AI, ensuring equal outcomes regardless of gender, region, or language, using resume screening examples to focus on skills and experience.

Discover unexpected, non-scripted AI behaviors through exploratory testing, document reactions and assumptions, and practice curiosity-driven, scenario-based testing with real-time password-reset scenarios.

Balance speed and accuracy under load by testing performance and accuracy, managing peak traffic, avoiding hallucinations, and meeting SLA and resource constraints for reliable AI.

Engage in a hands-on assignment by testing public AI chatbots like ChatGPT, Bird, or Copilot; observe patterns and unexpected outputs, applying all course testing types.

Document test evidence for AI apps by recording environment, model version, dataset, and logs; capture input variations and observed, non-deterministic output patterns to reveal risk, not just pass or fail.

Explore automation testing in AI, focusing on partial automation with data validation, API testing, and performance checks, using Python, pytest, and Great Expectations, while balancing human judgment.

Learn how generative AI creates unique outputs from prompts, emphasizing evaluation over assertion and variability across text, image, video, and code.

Understand high-level prompt testing in generative AI by varying input wording, clarifying intent, and changing context. See how prompts yield different outputs—from simplified explanations to technical details.

Expose how to perform hallucination testing in Gen AI, identifying fake facts and confident but wrong answers, with finance and health care risk considerations and practical examples.

Discover toxicity and safety testing for generative AI, ensuring safety behavior is mandatory and that the system refuses unsafe prompts like hate speech, violence, or harmful content.

Study guardrail testing to verify AI respects policy boundaries and ethical restrictions, ensuring controlled responses. Learn how disclaimers and professional guidance appear in sensitive prompts.

Learn how response consistency testing checks that generative AI maintains meaning across repeated questions and multi-turn prompts, yielding mostly consistent, content-stable outputs while slight variations occur.

Explore long conversation and memory testing to assess AI's context retention and consistent recall, showing how AI remembers names and earlier messages and integrates context.

Evaluate GenAI with a hybrid approach, checking relevance, accuracy, safety, tone, and completeness. Combine automated checks with manual evaluation and evaluation datasets to ensure thorough, reliable testing.

Identify AI system type, assess risks, set acceptable thresholds, design variation tests, and monitor continuously to approach any AI project and protect users from wrong intelligent decisions.

Define acceptable range with tolerance and confidence thresholds; thoroughly test data for missing values, duplicates, and bias; combine manual and automated testing; prioritize high-risk scenarios; implement continuous monitoring after retraining.

learn how to prompt effectively by treating ai like a smart intern, where clear input and context drive better outputs; discover that prompting is simple communication with ai, not coding.

Define a prompt as a simple instruction you give to ai, and learn that clear, contextual prompts with audience and examples yield better results.

Prompting skills shape AI outputs with clarity and appropriate depth, reducing rework. They multiply your thinking and speed up results, making you a faster, more effective learner or tester.

Discover the four-part prompt formula: role, task, context, and format for writing effective prompts. Assign a role, define the task and context, and pick a format to improve results.

Compare poor prompts lacking role, context, format, and depth with strong prompts that specify audience, real-world examples, and structure. Learn how effective prompting enhances regression testing explanations for a beginner.

Improve prompt quality by using a prompt formula with clear role, context, and format, showing how login page scenarios yield precise test cases.

learn to build a test automation framework from scratch using Java, Maven, TestNG, Selenium, and REST Assured by planning architecture, folder structure, and data strategy before coding.

Explore different prompting styles to tailor AI instructions, from zero-shot to few-shot, one-shot, chain of thought, and compound prompting, with simple real examples to boost results.

Master zero-shot prompting by asking directly without examples to test AI general knowledge and speed. Understand its use for quick information and the trade-off of fewer tokens with generic outputs.

One-shot prompting shows an example and lets AI follow a pattern, enabling consistent output and format control for tasks like writing test cases with scenario, steps, and expected result.

Learn few-shot prompting by providing 2–3 examples to teach ai patterns, ensuring consistency and formatting across test cases, such as valid/invalid scenarios, and applying it to tasks like forget password.

Use chain-of-thought prompting to make AI think step-by-step and improve reasoning accuracy for debugging and root-cause analysis. Analyze logs and inputs to guide architecture decisions.

Explore compound prompting by combining roles, examples, reasonings, and steps to tackle complex tasks. See how it guides framework, folder structure, and approval before implementation to control quality and cost.

Explore prompting techniques from zero-shot, one-shot, and few-shot prompting to chain-of-thought and compound prompting, and learn to match the right approach to the problem for better outputs.

Understand that every AI request costs tokens, the fundamental unit of AI communication, and that bad prompting wastes time and money in both personal learning and enterprise use.

Explore the test automation framework and page object model, including core components like test scripts, page objects, utilities, and config. Learn data-driven, keyword-driven, hybrid, and BDD approaches with Gherkin.

Understand the pain of not using a page object model and learn to build a maintainable BDD framework with AI tools and reusable locators.

Learn to build a page object model and bdd framework from scratch using tools like ChatGPT, Java, Selenium, and Cucumber, including login and add-to-cart test on sauce lab demo website.

Build an automation framework in 10 minutes by creating a driver factory under utils, adding hooks, and implementing login and cart feature files with page objects and a test runner.

Build a reusable test automation framework with a page object model and cucumber bdd, featuring a login page and a test runner for feature files.

Create a cucumber bdd framework from scratch with an ai agent, using Java, Selenium, and page object model. Use Copilot to plan the project and craft a readme for reference.

Build a phase-wise plan and implement it with an AI agent, starting UI automation with page object model and Java Selenium, then API automation with Rest Assured, with iterative reviews.

Show how to fix import errors with copilot using compound prompting, reload the IntelliJ project, and run tests in auto mode until all seven tests pass.

Build a one-click automation test using a run.sh script to execute seven maven tests. Leverage GitHub Copilot to design a framework, run tests, and generate a detailed report.