QAstra Academy

The QA Course That Puts You Five Years Ahead of Everyone Else.

AI QA roles are growing 48% faster than industry average with a $145K median salary for senior AI test engineers in the US (AI LLM Testing, Feb 2026). The most advanced course in India's QA training landscape.

AI is not just changing how we test software — it’s changing what software is. This course has two dimensions: first, how to use AI to become a dramatically more productive QA engineer; second, and more importantly, how to test the AI systems themselves. You’ll learn to evaluate LLM responses, detect hallucinations and bias, test RAG pipelines, break AI systems with adversarial prompts, and audit them for ethical compliance. If you’re a senior QA engineer, this is where your next promotion lives.

Syllabus

Part 1 — Become an AI-Powered QA Engineer (Phases 1–4)

Use AI tools to design smarter tests, write automation faster, eliminate flakiness, and debug with precision.
Phase 1: GenAI Fundamentals & Prompt Engineering for QA

Modules

GenAI Fundamentals

Prompt Engineering 101

Advanced Prompting

QA Personas

Prompt Management

Phase 2: AI-Assisted Test Design & Manual Testing

Modules

AI Test Scenario Gen

Test Case Optimization

Edge Case Discovery

Test Data Generation

AI in Bug Reporting

Phase 3: AI-Powered Automation with Copilot & Cursor

Modules

AI for Code Logic

Copilot/Cursor Basics

Script Generation

AI Code Review

Fixing Flaky Tests

Phase 4: Visual Testing, Self-Healing & AI Analytics

Modules

AI Visual Validation

Self-Healing Tests

AI API Testing

AI Test Analytics

Part 1 Project

Part 2 — Test AI Applications Like an Expert (Phases 5–8)

DeepEval and Ragas are now the industry-standard open-source frameworks for LLM evaluation (Confident AI, Apr 2026). This is the only course in India that teaches both — in production context, not just as demos.
Phase1: LLM Internals — How AI Applications Work

Modules

LLM Internal Mechanics

RAG Systems 101

Prompt Injection Intro

Model Hallucinations

Determinism vs Stochastic

Phase2: LLM Evaluation — Ragas, DeepEval & Benchmarking

Modules

Evaluation Metrics

Ragas Framework

DeepEval Basics

Human-in-the-loop

Model Benchmarking

Phase3: AI Safety — Jailbreaking, Bias, PII & Ethics

Modules

Jailbreaking & Safety

Bias & Toxicity

PII & Privacy Testing

Adversarial Testing

Ethical AI Compliance

Phase4: Agentic AI Testing — Multi-Agent & Autonomous QA

Modules

AI Agent Workflows

Multi-agent Testing

Latencey & Performance

Cost & Token Audit

Final Capstone

Outcome

This course equips you with two sets of rare, in-demand skills:

After Part 1 — AI-Powered Testing, you will be able to:

  • Generate test cases from requirements using prompt engineering and GitHub Copilot
  • Build automation scripts 3× faster with AI-assisted code generation
  • Implement self-healing test frameworks that adapt to UI changes automatically
  • Use AI to detect flaky tests, summarise logs, and root-cause failures in minutes

After Part 2 — Testing AI Applications, you will be able to:

  • Evaluate LLM outputs for accuracy, hallucination, and consistency using Ragas and DeepEval
  • Test RAG pipelines for retrieval quality and response faithfulness
  • Execute adversarial testing — prompt injection, jailbreaking, and boundary attacks
  • Audit AI models for bias, toxicity, PII leakage, and ethical compliance
  • Test multi-agent AI workflows for latency, cost efficiency, and reliability
  • Trace and debug RAG pipelines using LangSmith observability
  • Benchmark LLM cost-per-query across GPT-4o, Claude, and Llama for business decisions.
  • Audit AI systems for EU AI Act compliance — risk categorisation and documentation requirements

Career roles you’ll be ready for: AI QA Engineer, LLM Quality Engineer, Senior SDET, QA Tech Lead (AI Systems), AI Safety Tester

This is not just a course upgrade — it is a career category change.

Tools

GitHub Copilot

Cursor IDE

LangChain

n8n

Playwright

Applitools

ChatGPT

Claude

Gemini

Who Should Enroll

Senior QA / automation engineer →

Transition to AI QA.

SDET →

Add LLM evaluation and AI safety testing layer.

QA lead →

Future-proof your team's AI testing skills.

Developer / ML engineer →

Add formal QA methodology for AI systems.

Market Growth

faster growth for AI QA roles vs industry average' (AI LLM Testing Feb 2026).
0 %
median salary — senior AI test engineer, US' (Glassdoor/BLS 2026).
$ 0 K
Job postings mentioning GenAI skills grew 800% since 2022' (LinkedIn 2025)
0 %

FAQs

This course is designed for experienced QA engineers, test automation engineers, and software testers who want to transition into AI-driven testing and modern QA practices.

No prior AI knowledge is required. The course starts with AI and LLM fundamentals and gradually moves toward advanced AI testing concepts and tools.

You will learn GitHub Copilot, Cursor IDE, Playwright (AI-generated), Ragas (RAG evaluation), DeepEval (LLM unit testing), LangChain + LangSmith (RAG pipelines + tracing), n8n (workflow automation), Applitools (visual AI), ChatGPT / Claude / Gemini, custom adversarial prompt libraries, EU AI Act compliance frameworks.

Yes. The program includes real-world AI SaaS testing projects, allowing you to practice AI testing strategies, automation workflows, and model validation techniques.

You’ll use Ragas and DeepEval for LLM evaluation, LangChain for RAG pipeline testing, n8n for automation workflow validation,Also LangSmith for LLM tracing and observability, Confident AI as the production-grade evaluation platform, and custom adversarial prompt libraries for red-teaming. These are the tools used at frontier AI companies (Anthropic, OpenAI, Cohere) for safety and quality evaluation.

Yes, with one caveat — this is an advanced course and we recommend at least 2 years of QA experience before joining. Part 1 starts with GenAI fundamentals and requires no prior AI knowledge. By the time you reach Part 2, you’ll have the conceptual foundation to engage with LLM testing confidently.

Most courses add a 2-hour ‘AI tools’ chapter at the end. This is an entirely separate, dedicated curriculum. Part 1 is 4 full phases of AI-powered testing productivity. Part 2 is 4 phases of testing actual AI systems — a skill that literally does not exist in most other QA courses in India. The curriculum is built on the tooling and evaluation frameworks used by AI companies, not just general productivity tips.

Ready to Start Your QA Journey?

New batches starting every month. Limited seats per batch. Enquire today.

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