Developing GitLab Duo: How we validate and test AI models at scale

This post details GitLab's AI model validation process for GitLab Duo features. It outlines the creation of a prompt library as a proxy for production, the process of baselining model performance using metrics like Cosine Similarity Score and LLM Judge, and the iterative development cycle involving focused and broader dataset testing. The post emphasizes the importance of testing at scale to identify patterns and mitigate risks associated with LLMs, and highlights the use of models from Google and Anthropic. It also mentions the Centralized Evaluation Framework (CEF) and its role in identifying patterns and assessing overall behavior.