When I was working on Vector Search with JavaScript, vector search was a hot topic. By the time the book was published some people had begun saying that because of LLMs and their advances, we have moved beyond vector search.
This couldn't be farther from the truth. LLMs and agentic development is amazing, but it often gets things wrong. They don't fail because the model is weak always, but they fail because the right context can be sitting somewhere else and they had no idea that it existed.
Your docs are in one place. Tool outputs are in another. Prior decisions are in chat history, issue comments, AGENTS.md, local files, and half a dozen API responses. You can paste more into the prompt, but that gets expensive and messy fast.
Vector search gives agents a memory layer they can inspect, query, move, and rebuild.
That still matters in an LLM-first world.
