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
I've been building web applications for 8 years, and locale handling has always been my silent killer. Date formats break in production, currency symbols get mangled, timezone calculations drift—all caught too late. That's why I took TestSprite for a real project spin. Integration testing is boring, expensive, and brittle. Write 100 Selenium tests, change the UI once, watch 60 fail. Most teams eit
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