Timely analysis, tutorials, and opinion from ScaleCloud architects on the cloud and AI landscape.
10 resources
Pure vector similarity search retrieves semantically similar chunks, but similarity isn't the same as relevance — a chunk can be topically similar to a…
A single-page checklist walking through five key decisions with the tradeoffs for each option listed alongside it, so teams can make an informed choice…
For each chunking strategy, the notebook computes retrieval precision at k against a labeled question set you provide, giving a side-by-side comparison rather than…
Document chunking and embedding, setting up a vector store, and wiring a retrieval chain to a language model with source citation included in the…
Use structure-aware chunking (respecting headings, tables, and document sections) rather than fixed-length character chunking, which materially improves retrieval quality on struct
Most public RAG tutorials stop at "it returns a plausible answer" — this eBook continues past that point into evaluation methodology, access control, citation,…
The standard pipeline includes a document ingestion and chunking stage, an embedding stage, a hybrid (vector plus keyword) retrieval stage, and a generation stage…
An illustrative scenario showing how a digital native company hardened a RAG-based support assistant for production customer traffic.
