Fixed-length chunking requires no external services, yet semantic chunking absolutely needs an Embedding API — why? The core idea of semantic chunking is to split text at semantic boundaries. Determining whether "two pieces of text belong to the same topic" requires converting text into vectors and computing similarity — that's exactly what the Embedding API does. Dimension Fixed-Length / Recur
We had ArgoCD running perfectly. Every deployment was reconciled from Git. Drift detection worked. Rollbacks were one-click. Our GitOps setup was clean. Developers still couldn't provision a staging environment without pinging the platform team. That gap — between "GitOps in place" and "developers can actually self-serve" — is where most platform engineering teams get stuck. GitOps solves a real p
Why Does Switching Embedding Models Make Such a Huge Difference? In the first four articles, we built the RAG pipeline, tuned parameters, and mastered chunking strategies. But there's one question we haven't dived into: After your documents are chunked, how do they become vectors? This process is called Embedding. It transforms human-readable text into machine-computable vectors. The choice of E
Anthropic now ships at least three different memory models inside the Claude product family, and they don't behave the same way. Claude.ai has a chat memory feature for Pro, Max, Team, and Enterprise users that summarizes prior conversations and injects that summary into new chats. Claude Code has CLAUDE.md files plus a separate "auto memory" directory the model writes to itself, both loaded at se
Part 2 of 5 in The New Engineering Contract - what it means to lead engineers when AI is doing more of the coding. Stripe never skipped the boring stuff. They ship 1,300 AI PRs a week. Amazon skipped it. Their storefront went down for six hours. Kent Beck wrote the answer in Extreme Programming Explained in 1999. We read it. Then chose velocity anyway. A friend of mine leads engineering at a funde