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
"OK, I understand the RPS formula. But is our RPS — actually — high or low compared to our industry?" Right after I published the RPS-definition guide last week, this was the most common question I got back from EC operators. They want to know where they sit, not just how to compute the number. Knowing your RPS is $1.20 means nothing if you don't know whether that's the industry median, the top qu
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