Evaluating RAG Performance for Storing AI Compiler Error Explanations in Vector Databases

On November 12, 2026, during Day 5 of MLH Global Hack Week: Builders Week, the author of the Build Buddy project ran a hands-on test of how well a RAG system works when AI-generated explanations are stored in a vector database. Throughout the week leading up to that day, Build Buddy had been collecting explanations from AI models about C# compiler errors. The explanations were not uniform in quality: some were correct, while others were confidently wrong. To see what happens when such content is searched the way a RAG system would search it, the author took 12 real AI explanations of compiler errors and placed them in a vector database. The 12 explanations had been produced during the week by Gemma 3 4B and Grok 4.7. Each explanation was reviewed manually and classified as correct, wrong, or inconsistent before being embedded locally with nomic-embed-text. The test showed that semantic retrieval can effectively separate compiler errors that look similar in code but stem from different causes. The material covers only this completed retrieval experiment and its finding; it does not describe any follow-up iteration, larger-scale evaluation, or further development beyond this stage.