JavaScript Fundamentals for QA & Automation Testers

Explore artificial intelligence as a human-like computer system that learns from data, understands language, recognizes images, and can generate test cases for QA and automation.

Explore how humans and AI collaborate in testing and development, assess AI reliability, and leverage tools like Postman and SoapUI with a human-in-the-loop approach to boost efficiency.

Discover how large language models function as a human-brain-like AI trained on vast data to translate, answer, summarize, and generate content, and how they differ from Google.

Explore how retrieval augmented generation enhances LLMs by querying a vector database and a knowledge base to deliver accurate responses without full retraining.

Explore how generative AI creates new content, unlike traditional AI that analyzes and retrieves from existing data. See examples such as generating code, documents, images, audio, and video.

Explore how an AI memory stores, retains, and retrieves information from past interactions to power meaningful chatbot conversations, distinguishing short-term from long-term memory.

Discover how an AI agent operates by using tools, making independent decisions, and pursuing a goal, illustrated with a travel planning example and LLM reasoning.

Explore how LangChain connects LLMs to external data sources via vector databases, with LangGraph as a memory-driven, node-edge workflow for AI agents using nodes, edges, states, and checkpoints.

Overview on MCP presents the model context protocol as a universal adapter that standardizes how AI models call tools, APIs, and databases, enabling safe, reusable, scalable tool usage.

Learn how human in the loop adds human approval to AI decisions in critical tasks, and how guardrails, prompts, and awareness of hallucination prevent unsafe outputs.

master the fine-tuning concept to tailor an AI agent’s interactions for customers, branch staff, and bank officials, and contrast it with RAG, which relies on external knowledge.

Understand how context drives accuracy in language models and learn to provide precise information to guide outputs, with examples.

Learn the fundamentals of prompt engineering and how clear, context-rich prompts prevent AI hallucinations, guiding precise responses and actionable plans like booking trips.

Define a context file that assigns a tester role and mandatory rules for the LLM, then analyze acceptance criteria and UI mockups to generate manual or automation test cases.

Explore creating a manual context file and using an llm to generate manual test cases for the sourcedemo.com login flow, covering positive and negative scenarios and acceptance criteria.

Explore different prompt types used in automation testing, including zero-shot, one-shot, few-shot, and chain of thought. Learn how clear instructions and context files guide LLMs to generate accurate outputs.

Compare ChatGPT, Copilot, and Cursor for coding assistance in automation testing and development. Cursor provides codebase-aware, integrated AI tooling, outperforming general ChatGPT and standalone Copilot for complex solutions.

Discover how OpenAI powers ChatGPT and Copilot, explain its mission to advance artificial general intelligence for humanity, and outline its practice of publishing research and sharing principles.

Explore how ai models are trained on data to recognize patterns and generate human-like text, such as gpt, and how llms are specialized tools within the ai toolbox.

Generate and secure an OpenAI API key after signing up, copy the secret key, and connect OpenAI to n8n to create AI agents.

Create a simple n8n workflow that connects to Gmail, reads data from a Google sheet, uses an ai agent with GPT-4.1 mini to filter sam and send an email.

Create a n8n workflow that reads data from Google Sheets sheet 2, checks status s, and sends background verification emails to candidates via AI-driven email actions.

Learn how n8n enables AI workflow automation, create a trial account, build AI agent workflows, and automate emails and data tasks without coding.

Learn how to set up a Jira cloud testing account, create a project, generate an API token, and integrate Zephyr squad for test management and bug tracking.

Demonstrate building an n8n workflow to create Jira defects from data, including credentials setup, URL handling, and automated creation of bugs when sheet status is new.

learn how to create a public chat in an n8n workflow, connecting to an ai model and memory to create defects from sheet data. monitor status updates as it runs.

Understand how OpenAI tokens define the input, how token counts drive processing time and cost budgeting, and how rate limits affect trial accounts.

Create a Chrome extension with Cursor AI to record and playback browser actions, generating test scripts for Playwright TypeScript or Selenium, with downloadable scripts and playback options.

Design and simulate an OTP shield app that monitors SMS, calls, and WhatsApp messages for OTP requests, warns users, and blocks suspicious APK files, with Android Studio and Gradle workflows.