Included with the couse

1. Complimentary eBook
Python from Scratch – A Practical Guide for AI Beginners
A structured refresher designed to help learners align Python skills with GenAI system development.

2. Comprehensive Course Material
Well-structured learning resources for every module, including explanations, architecture notes, and fully working code used during the sessions.

3. Alumni Status on Completion
Upon successful course completion, participants become part of the AI Mentorship Hub alumni network.

4. Free Course Upgrades
Any enhancements or updates made to this course in future batches will be available at no additional cost to alumni.

5. Ongoing Industry Updates & Job Opportunities
Alumni receive continuous updates on industry trends, emerging GenAI roles, and relevant job opportunities shared through the community.

6. Alumni Privileges & Discounts
Eligible alumni discounts on future courses and advanced programs, as applicable.

Why GenAI for Testing Engineers Now

AI Is Changing What QA Engineers Are Expected to Do

Test teams are no longer just asked to find bugs — they are expected to build intelligent testing systems. QA engineers who can work with LLMs, build AI agents, and validate AI outputs are among the most sought-after profiles in the current market.

Tool Users Get Replaced. Builders Get Hired

Most testing engineers who explore AI stop at using ChatGPT to generate test cases. The roles that grow — and the salaries that reflect it — belong to engineers who can build prompt pipelines, deploy AI test agents, and put guardrails around AI outputs. This program is built to close that gap

AI Testing Is a New Discipline — and It Needs Testing Engineers

Someone has to test the AI systems being shipped. That requires people who understand both testing principles and how LLMs behave. That intersection is where this course lives.

"The testing engineers who learn to build and validate AI systems will define quality engineering for the next decade."

Who This Course Is For

• QA engineers and manual testers who want to move into AI-augmented testing workflows

• SDETs and automation engineers ready to layer GenAI capabilities onto their existing skills

• QA leads and managers who want to evaluate and adopt AI testing tools with confidence

• Developers with a testing focus who want to build AI-powered test utilities and agents

What You Will Build

• Prompt-Controlled Test Case Generator: An AI assistant that takes a feature description and produces consistent, structured test cases — not freeform suggestions

• AI-Powered Bug Triage Assistant: Input raw defect descriptions and get structured severity classification, component tagging, and suggested priority — ready for your defect tracker

• Test Knowledge Base Assistant: An AI system that answers questions only from your internal test plans, runbooks, and defect history — grounded, not hallucinated

• Decision-Making Test Agent: An agent that reads a requirement and decides what to test next

• Capstone — AI Test Automation Agent: Reads feature specs, generates test cases, identifies coverage gaps, and produces test reports your team can trust

• Deployed AI Test Agent as a REST API: Wrap your agent in FastAPI and expose a /generate-tests endpoint — your team can use it without touching code

Course Journey

→ LLLM fundamentals — tokens, temperature, context windows, failure modes
→ Prompt engineering as a structured, repeatable testing skill
→ RAG pipelines — teach AI to answer from your own test documentation
→ Agentic AI — observe, decide, act, respond loops with tool usage
→ Capstone AI Test Agent — end-to-end, production-grade
→ FastAPI deployment — expose your agent as a REST service
→ Guardrails, reliability, and responsible AI testing

→ Evaluation and monitoring — RAGAS, LLM-as-judge, prompt regression testing

Frameworks & Tools

Python · FastAPI · LangChain · LangSmith · Guardrails AI · Pytest · Pydantic · Vector Databases · RAGAS · Postman · OpenAI / Anthropic API

Why GenAI and LLM Engineering With Us

Build your GenAI engineering career the right way — Build your AI testing career the right way — by designing and deploying real systems, not following along with pre-built demos. This program focuses on hands-on engineering, production deployment, and a learning experience that mirrors how AI testing tools are actually built and shipped.

Real-World Projects

Expert Mentorship

Personalized Learning

Learn directly from practitioners with real experience building and shipping AI systems.

Small batches of ~20 participants. Focused guidance and direct access to the mentor throughout

Apply every concept to a live QA problem. Every module ends with something you can show

Frequently asked questions

Do I need prior AI or ML experience?

No. The course starts from how LLMs work and builds up progressively. Familiarity with software testing and basic Python will help you get the most out of it.

Is this relevant if I only do manual testing?

Yes. Prompt engineering, bug triage AI, and the knowledge base assistant modules are highly applicable without deep coding. The later modules do involve Python, but you will be guided through every step.

How is this different from a general GenAI or LLM course?

Everything in this course is built around testing problems. You are not learning LLMs in the abstract — you are learning how to apply them to test case generation, defect classification, coverage analysis, and quality reporting.

Will I actually deploy something real?

Yes. You will deploy your AI Test Agent as a FastAPI service, expose an API endpoint, and call it from Postman or a simple frontend. Deployment is not optional here — it is part of the curriculum.

Do I need paid API accounts or cloud tools?

Minimal cost. LLM API usage during the course is small. You will be guided on setup and cost controls from day one.

Will sessions be recorded?

Yes. All sessions are recorded and available within 24 hours. Downloadable course materials are included

How many seats are available per cohort?

Each cohort is intentionally limited to around 20 participants to maintain interaction quality and hands-on guidance.

What roles does this course prepare me for?

AI QA Engineer, GenAI Test Engineer, LLM Quality Engineer, SDET with AI skills, QA Automation Lead, AI Systems Tester.