AI QA Assistant: An Intelligent Auxiliary Tool for Test Automation Based on RAG

The AI QA Assistant project combines large language models with a knowledge base of test automation best practices. Using Retrieval-Augmented Generation (RAG) technology, it addresses core challenges for QA engineers such as high maintenance costs of automated tests and low debugging efficiency, helping to build more stable automated tests and quickly debug Selenium issues.

In test automation practice, teams often fall into the "automation quagmire", facing issues like fragile test scripts, high maintenance costs, and obscure debugging information. As a mainstream web automation framework, Selenium has common errors such as element location failures and improper waiting strategies that require manual troubleshooting, leading to a steep learning curve and high debugging complexity.

The core technology of the project is Retrieval-Augmented Generation (RAG). Unlike pure generative LLMs, it first retrieves relevant information from an external knowledge base as context. Its advantages include answers based on the latest verified best practices, high credibility and verifiability of answers. The knowledge base focuses on the field of test automation, covering Selenium tips, common error patterns, and other content.

Combining large language models with a knowledge base of test automation best practices, it helps QA engineers build more stable automated tests and quickly debug Selenium issues