• views: 111
• date: 27 July 2025
• posted by: 0nelove

QA Engineers who want to bring AI into their toolkit
Automation testers tired of redundant test cases
Python developers interested in real-world LLM applications
Anyone curious about semantic search, embeddings, or LLM-powered utilities
Requirements
Basic understanding of Python (3.x)
A Gemini API key (we'll guide you on how to get it)
A sample test cases CSV (included in course resources)
Description
Hi there, and welcome to "AI for QA: Detect Duplicate Test Cases Using AI" — a hands-on course where we combine the power of Python, Large Language Models (LLMs) like Gemini, and vector similarity to solve a real-world problem in QA and software testing.If you've ever worked with hundreds of test cases and wondered:"Am I repeating the same test over and over with slight wording differences?"then you're in the right place.What Are We Building?In this course, you're going to build a Python-based utility that reads a CSV file of test cases — and intelligently finds semantically similar or duplicate ones using:Text Embeddings from Gemini AICosine Similarity for vector comparisonSmart logic to detect similar titles, steps, and expectationsAnd in the end, you'll have a tool that can:Detect overlapping test casesHighlight duplicate coverageHelp clean up bloated test repositoriesWhat You'll Learn (Hands-On):By the end of this course, you'll be able to:Parse raw test case CSV files into structured Python objectsUse Gemini embeddings to convert titles and actions into semantic vectorsApply cosine similarity to detect which test cases are actually "similar in meaning"Set thresholds to filter only truly overlapping scenariosExport results into JSON or other readable formats
Who this course is for
QA Engineers who want to bring AI into their toolkit
Automation testers tired of redundant test cases
Anyone curious about semantic search, embeddings, or LLM-powered utilities
You don't need prior experience in machine learning — if you know basic Python and CSV handling, you're all set!



   
   
   

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