Generative AI for Manual & Automation Testers: Selenium, Playwright& AI Agent Development – Live Training
(Master AI, ML, Fine-Tuning, Prompt Engineering, AI Agents & Test Automation — then land the job)
The Generative AI, AI Agents & AI-First Engineering program is designed to help professionals build practical expertise in the most in-demand AI technologies shaping the future of software development and testing. Participants will gain hands-on experience with Generative AI, Large Language Models (LLMs), Prompt Engineering, AI Agents, RAG (Retrieval-Augmented Generation), Vector Databases, MCP (Model Context Protocol), AI Testing, and AI-powered automation. The course focuses on real-world implementation, enabling learners to understand how modern AI systems are designed, developed, tested, and deployed in enterprise environments.
Through live sessions, hands-on exercises, industry use cases, and project-based learning, participants will learn how to leverage AI across the software development lifecycle, build intelligent applications, automate workflows, create AI-driven solutions, and apply AI-first engineering practices. Whether you are a Software Engineer, Tester, Automation Engineer, Business Analyst, DevOps Professional, or technology enthusiast, this program provides the skills and practical knowledge required to confidently work on AI-powered projects and accelerate your career in the rapidly evolving AI ecosystem.
Live Sessions Price:
For LIVE sessions – Offer price after discount is 200 USD 159 119 USD Or USD15000 INR 12000 INR 7900 Rupees.
OR
Free Demo Session:
Indian Timings: 15th June @ 9 PM – 10 PM (IST) (Indian Timings)
U.S Timings: 15th June @ 11:30 AM – 12:30 PM (EST) (U.S Timings)
U.K Timings: 15th June @ 4:30 PM – 5:30 PM (BST) (UK Timings)
Class Schedule:
For Participants in India: Monday to Friday @ 9 PM – 10 PM (IST)
For Participants in the US: Monday to Friday @ 11:30 AM – 12:30 PM (EST)
For Participants in the UK: Monday to Friday @ 4:30 PM – 5:30 PM (BST)
What will I learn by the end of this course?
- Understand the concept of AI-First Engineering and how AI is transforming software delivery
- Identify opportunities to apply AI across the Software Development Lifecycle (SDLC)
- Use Prompt Engineering and Context Engineering techniques effectively
- Apply Spec-Driven Development in AI-assisted software projects
- Understand and implement Agentic Workflows and BMAD-style Agile practices
- Create AI-ready requirements, specifications, plans, and task breakdowns
- Design reusable instruction files and engineering assets for AI-assisted development
- Define and utilize AI agent roles such as Architect, Developer, QA, Security, Documentation, and Release Manager
- Work with IDE-native AI development tools and agentic coding workflows
- Leverage AI for requirements analysis, design, coding, testing, documentation, and software release activities
- Understand MCP (Model Context Protocol) and how AI agents connect with tools, systems, and engineering workflows
- Apply governance, security, privacy, compliance, and quality gates in AI-driven software development environments
- Build practical skills through real-world use cases, demonstrations, and project-based learning
What student’s have to say about Sabrish Surender:
| The trainer explained AI First Engineering concepts with excellent real-time examples, practical workflows, and strong industry knowledge. – Ram
The trainer explained all concepts in a very clear and practical manner with real-time industry examples. The sessions on AI First Engineering, Prompt Engineering, Spec Driven Development, and Agentic Workflows were highly informative and easy to understand. Hands-on demonstrations and project-based explanations made the learning experience very engaging and useful. – Micheal The course content was very well structured and covered modern AI engineering concepts in a practical way. The trainer explained every module clearly with real-time examples and interactive sessions. I gained strong confidence in using AI tools and workflows in software projects. – Priya |
Salient Features:
- Live Instructor-Led Training with Interactive Sessions
- Hands-on Practice with Generative AI, AI Agents, and AI-First Engineering Concepts
- Real-World Projects, Use Cases, and Practical Demonstrations
- Industry-Relevant Curriculum Designed for Software and QA Professionals
- Course Completion Certificate
Who can enroll in this course?
- Software Developers and Engineers
- Manual and Automation Testers
- QA Engineers and Test Leads
- Business Analysts and Product Owners
- DevOps and Cloud Professionals
- Technical Architects and Engineering Managers
- Students and Fresh Graduates interested in AI
- Any IT Professional looking to build a career in Generative AI and AI-First Engineering
Course syllabus:
Week 1
Day 1: Welcome to AI (1 hr)
- Introduction to AI & Machine Learning
- Types of ML: Supervised, Unsupervised, Deep Learning
- Datasets, parameters, labels & the 80-20 rule
- Explore 4 providers: Gemini, GPT, Claude, Llama
Outcome: Understand what AI/ML is and try your first models.
DAY 2: Inside an LLM (1 hr)
- What is an LLM: parameters, layers & accuracy
- Public vs Private LLMs & local deployment with Ollama
- Hallucinations & challenges in Gen AI
- Rate limits, context windows & aggregators
Outcome: Know how LLMs are built and where they run.
DAY 3: Generative AI in Action (1 hr)
- How Gen AI generates text, images & video
- Deep dive into GPT, Claude, DeepSeek & Llama
- CPU vs GPU — why hardware matters
- Comparing models on a model arena
Outcome: Generate your first AI content across providers.
DAY 4: Gen AI for Testing (1 hr)
- Applications of Gen AI in software testing
- Case studies: ML & DL in the real world
- Linear regression vs classification, simply explained
- Collaborative vs content-based filtering
Outcome: See how AI applies directly to QA work.
DAY 5: First LLM Workshop (1 hr)
- Guided exploration of GPT, Claude & Gemini
- Choosing the right model for the right task
- Week 1 recap & knowledge check
- Q&A and prep for LLM internals
Outcome: Confidently navigate any major LLM chat agent.
Week 2:
DAY 1: LLM Anatomy (1 hr)
- Input processing: prompt → tokens → embeddings
- Vector embeddings & numerical representation
- Semantic search & meaningful matching
- Tokenizers & counting tokens hands-on
Outcome: Understand how text becomes numbers a model reads.
DAY 2: Transformers & Attention (1 hr)
- The transformer architecture explained simply
- Self-attention: how context disambiguates words
- Feed-forward networks & layers
- Why more layers means more accuracy
Outcome: Grasp the engine that powers every modern LLM.
DAY 3: Training Process (1 hr)
- Pre-training: collecting & learning from data
- Pre-training vs fine-tuning explained
- Data requirements & preprocessing
- Temperature & controlling output randomness
Outcome: Know how a model is trained and tuned.
DAY 4: Environment Setup (1 hr)
- Setting up your local & cloud LLM workspace
- Running models locally with Ollama
- Connecting to cloud providers safely
- Moderation & guardrails with Llama Guard
Outcome: A working AI development environment.
DAY 5: Ethics & Bias (1 hr)
- Ethical use of LLMs in testing
- Recognising & reducing bias
- Responsible AI practices
- Week 2 recap & hands-on exploration
Outcome: Use LLMs responsibly and explain how they work.
Week 3
DAY 1: Prompting Principles (1 hr)
- Introduction to prompt engineering principles
- Why prompts make or break AI output
- Anatomy of a great prompt
- Common mistakes & how to avoid them
Outcome: Write clear, reliable prompts from scratch.
DAY 2: The ICED-TO Framework (1 hr)
- The ICED-TO framework, step by step
- Techniques for effective prompt creation
- Building reusable prompt templates
- Consistent AI outputs for testing
Outcome: Apply a proven framework to any prompt.
DAY 3: Context & Intent (1 hr)
- Understanding context and intent in prompts
- Few-shot & role-based prompting
- Guiding tone, format & structure
- Iterating and refining prompts
Outcome: Steer AI to exactly the output you need.
DAY 4: Prompts for Testing (1 hr)
- Crafting prompts for testing scenarios
- Generating test data with AI
- Test case generation prompts
- Bug report & documentation prompts
Outcome: Generate test data & cases on demand.
DAY 5: Prompt Workshop (1 hr)
- Live prompt-building challenge
- Peer review & prompt improvement
- Building your personal prompt library
- Week 3 recap & prep for automation
Outcome: A reusable library of testing prompts.
Week 4
DAY 1: Fine-Tuning Basics (1 hr)
- What fine-tuning is & when to use it
- Supervised fine-tuning with labelled data
- Preparing a JSONL training dataset
- Base model vs fine-tuned model
Outcome: Build a dataset & understand fine-tuning.
DAY 2: Hyperparameters & Tuning (1 hr)
- Epochs: how many times the model sees data
- Learning rate multiplier explained
- Static fine-tuning vs dynamic RAG
- Running & validating a fine-tune job
Outcome: Tune a model and read its results.
DAY 3: Selenium & Playwright with AI (1 hr)
- Generating Selenium scripts using LLMs
- Generating Playwright scripts using LLMs
- Automating test script creation workflows
- Integrating LLM outputs into frameworks
Outcome: Auto-generate working automation scripts.
DAY 4: Building AI Agents (1 hr)
- What AI agents are & how they work
- Agent frameworks: LangChain & LlamaIndex
- Capstone kickoff: inspect-and-generate agent
- Test scenario generation for retail banking
Outcome: Start building your own AI agent.
DAY 5: Resume & LinkedIn Launch (1 hr)
- AI-optimized resume preparation
- Building a standout LinkedIn profile
- Applying to jobs on LinkedIn — a clear playbook
- Final capstone presentation & next steps
Outcome: A job-ready resume & an application strategy.