📚 AI Testing Learnings

Notes, experiments, lessons learned, and practical applications of Generative AI in Quality Engineering.

A collection of practical notes, experiments, and lessons learned while exploring Generative AI in Quality Engineering.

🎯 Goal

This repository documents my journey exploring:

• Prompt Engineering
• Retrieval-Augmented Generation (RAG)
• AI-Assisted Test Design
• Agentic Testing
• Model Context Protocol (MCP)
• Future AI-driven Quality Engineering practices

1. Prompt Engineering

• Fundamentals
• Prompt patterns
• Common mistakes
• Lessons learned

➡️ Read

2. RAG Basics

• Embeddings
• Vector databases
• Chunking
• Retrieval strategies

➡️ Read

3. AI Test Case Generation

• Requirement analysis
• Functional testing
• Security testing
• Automation assistance

➡️ Read

4. Agentic Testing

• AI Agents
• Tool Calling
• Autonomous workflows
• Future of testing

➡️ Read

5. MCP Notes

• Model Context Protocol
• Architecture
• Use cases
• Testing applications

➡️ Read

---

Author

Dinesh V P

Senior Automation Engineer | GenAI-Driven QA

LinkedIn | GitHub