Cost Optimisation Strategies for Enterprise RAG Pipelines

abstract

A retrieval pipeline that is correct but uneconomical does not reach production. This essay collects the techniques that reduce inference cost in production RAG systems without giving up retrieval quality or explainability: routing by task rather than by default, caching at the semantic layer instead of the prompt layer, structuring retrieval so that the expensive model sees less but better context, and measuring the whole thing in cost-per-answered-question rather than cost-per-token.

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