If your RAG candidate says "cosine similarity" before asking what the queries look like, end the interview. I do not mean that as a punchline. The order is telling you something. A candidate who reaches for the embedding model before they reach for the query log is solving a problem
The views and opinions expressed here are my own and do not reflect those of my employer. A few months ago I built a quick prototype. Semantic search over a stack of internal documentation and ticket history. The usual recipe: chunk, embed, throw it in pgvector, expose a small API,
I tried setting up claude-cognitive last week — an attention-based working-memory layer for Claude Code that promises persistent context across sessions on large codebases. The setup guide said fifteen minutes. It took three hours. The bug count was small. The pattern behind the bugs is what I want to write about,