GPT-5.2 dropped, and now your squad wants a Deep Knowledge Agent that can dig through internal docs—the sensitive stuff no one talks about publicly.

Next thing you know? You're drowning in chunking strategies, retrieval pipelines, vectordbs, and fifty different search approaches that all claim to be "the best."

Want something deployed by Friday? Here's the brutal truth: skip the rabbit holes. Pick a vector store that actually works with your stack. Test one chunking method—don't optimize prematurely. Build a basic retrieval flow first, then iterate.

Most teams waste weeks debating architecture. The ones shipping? They prototype fast, measure what breaks, then fix it. That's the playbook.