The original title is: "RAG Cost Estimates: Token Counts, Embeddings, and Node.js Semantic Search"

Short summary

A practical guide to estimating and controlling RAG costs by separating three meters: embedding indexing, retrieval-time work, and answer generation. The author recommends batch indexing, evaluating chunk sizes against recall before deploying, and keeping generation prompts narrow. Includes Python code for rough token estimation and warns about retry-induced duplicate writes needing idempotency keys.

• •Separate cost meters: embedding indexing, retrieval, and answer generation — the chat prompt is the budget risk, not embeddings
• •Test chunk size, overlap, and top-k against recall before celebrating a lower cost estimate
• •Retries around document indexing need idempotency keys — naive retries can create duplicate chunks that skew retrieval

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