What You'll Build & Learn
This is a build-focused Generative AI program — you won't just learn concepts, you'll ship real AI applications. By the end you'll be comfortable with:
- Python for AI — the practical foundations you need to build with AI
- APIs & LLMs — call large language models and integrate them into real apps
- Prompt Engineering — get reliable, high-quality results from AI models
- Retrieval-Augmented Generation (RAG) — ground AI answers in your own data
- AI Agents — build AI that can plan and take multi-step actions
- AI Application Development — turn models into working, useful products
- AI Testing & Evaluation — measure and improve AI quality and reliability
- Deployment — take your AI application from notebook to live
Who This Course Is For
Your QA and technical experience still has value — this course helps you build Generative AI skills on top of it.
QA Engineers & SDETs
Build on your existing QA and automation experience and expand into Generative AI, Python for AI, LLMs, RAG, AI agents and AI application development.
Working Technology Professionals
Developers, analysts, product professionals, technical leads and other technology professionals who want practical, hands-on Generative AI skills.
Career Transitioners
Professionals developing practical AI skills, real hands-on projects and a stronger foundation for AI-related opportunities.
Already in QA? Choose Your Next Path
Already working in QA? You have two strong ways to level up with QAGURU99 — pick the path that fits your goals.
Move toward Generative AI
Learn Python for AI, LLMs, RAG, AI Agents and AI application development.
Modernize Your QA Career
Learn Python, Pytest, Playwright, API automation, CI/CD, AI/LLM testing and DeepEval in our Modern QA program.
Training Format
- Live sessions
- Hands-on activities
- Weekly AI challenges
- Mini projects
- Final capstone project
Course Curriculum
Module 1: Introduction to AI & Modern Workplace Productivity
Session 1What is AI; ML vs Deep Learning vs Generative AI; understanding LLMs; AI capabilities, limitations, myths vs reality; responsible AI usage; choosing the right AI tool; the AI application lifecycle. Hands-on with ChatGPT and Google AI Studio.
Module 2: Prompt Engineering & AI Communication
Session 2Anatomy of an effective prompt; context, role, task, output; zero-shot, one-shot, few-shot; chain-of-thought; persona prompting; prompt templates; structured outputs (tables, JSON, reports); prompt optimization.
Module 3: AI for Everyday Work & Productivity
Session 3AI-powered workplace productivity; research with AI; document summarization; presentation generation; AI for Excel and data analysis; AI for communication and collaboration; best practices. Tools: ChatGPT, Google AI Studio.
Module 4: Python, APIs & GitHub Essentials
Sessions 4–5Python fundamentals (variables, conditionals, loops, functions, files); API fundamentals (REST, HTTP, JSON, authentication, API keys, calling LLM APIs); Git & GitHub (repositories, commits, branches, README, documentation, open-source best practices).
Module 5: Building AI Applications
Sessions 6–7Components of AI applications; input/output handling; prompt engineering within applications; structured responses; error handling; introduction to AI agents; connecting AI with external services. No-code platform: n8n. Build a chatbot, email assistant, FAQ assistant, meeting notes generator and content generator.
Module 6: Retrieval-Augmented Generation (RAG)
Sessions 8–9Why LLMs hallucinate; introduction to RAG; RAG architecture; embeddings; chunking strategies; vector databases (conceptual); similarity search; context retrieval; source grounding and citations; improving response accuracy.
Module 7: AI Evaluation, Testing & Responsible AI
Session 10Why AI applications need evaluation; functional testing; prompt testing; hallucination detection; response quality metrics; human evaluation; AI benchmarking; responsible AI; privacy and security; ethical AI usage; AI governance basics.
Module 8: Deployment, Monitoring & Cost Awareness
Session 11Preparing AI applications for production; FastAPI basics; Streamlit basics; deploying AI applications; hosting options; monitoring AI systems; logging and debugging; API usage monitoring; token management; cost estimation and optimization; scaling.
Module 9: Capstone Project & Future of AI
Session 12Future trends: AI agents, multimodal AI, voice AI, AI in software development, AI in business automation, emerging tools, and career pathways in AI. Participants design, build, test, deploy, and document a complete AI solution for a real-world business problem.
Capstone Requirements
- Define a real business use case
- Design the solution architecture
- Build the application using Python
- Integrate an LLM API
- Use n8n where appropriate
- Implement RAG for document-based use cases
- Evaluate AI responses
- Deploy the application
- Publish the project on GitHub
- Write professional documentation
- Present the solution
Weekly AI Challenges
- Week 1: Create an AI assistant that helps with your daily work.
- Week 2: Design reusable prompts for your profession.
- Week 3: Use AI to automate a repetitive workplace task.
- Week 4: Build a Python application that integrates an AI API and publish it on GitHub.
- Week 5: Create an AI assistant using n8n and develop a document-based chatbot using RAG.
- Week 6 (Capstone Project): Evaluate and improve your AI application's performance and reliability, deploy the application while optimizing operating costs, and present your completed capstone project with:
- Live demo
- GitHub repository
- Documentation
- Deployment link
- Evaluation results
- Lessons learned
Tools Used Throughout the Course
AI Platforms
No-Code Automation
Programming & Development
Suggested Capstone Projects
Live Generative AI Training in Dallas–Fort Worth
QAGURU99 delivers this Generative AI program live and instructor-led from our training location at 860 Hebron Parkway, Suite 701, Lewisville, TX 75057 — serving working professionals across the Dallas–Fort Worth metroplex, with live online access available. Whether you're in Lewisville, Carrollton, Plano, Frisco, Irving, Richardson, Denton or Dallas, you can join a live cohort and build real AI applications with hands-on guidance.
Frequently Asked Questions
Generative AI creates content — text, code, and more — from your prompts using large language models. In this course you build real, working AI applications with Python, APIs, LLMs, RAG and AI agents, finishing with a capstone project.
No prior experience is required. The course teaches the Python and API foundations you need as you build, so working professionals from many backgrounds can follow along.
Generative AI creates content from your prompts using large language models (LLMs). Agentic AI goes a step further: it uses those models to plan and take multi-step actions toward a goal, calling tools, APIs and data with limited human input. In this course you work with both — building Generative AI applications and moving into AI agents that can act, not just generate.
Yes. It's designed for QA engineers and SDETs, working technology professionals, and career transitioners who want practical, hands-on Generative AI skills built on their existing experience.
Classes are live and instructor-led — 12 live sessions over 6 weeks, 2 hours per session — with hands-on projects throughout.
QAGURU99 is based at 860 Hebron Parkway, Suite 701, Lewisville, TX 75057, and serves working professionals across the Dallas–Fort Worth metroplex, with live classes available online.
Yes — the program includes career support such as interview and resume preparation and guidance. This is support and guidance, not a guaranteed job.
Ready to build with AI?
Talk to an advisor about the next cohort and enrollment details.