1. The access collection black hole You need Figma access, Google Analytics, WordPress admin, GitHub, and the client's Slack. You ask. They forward a password email from two years ago. You ask again. Their developer says they'll get back to you. Three days pass. The fix: Send a single, complete access list on Day 1 — not "we'll need some access" but the exact list, with specifics for each tool,
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
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