PDCA cycle for improving RAG answer quality with Golden Data
RAG is easier to spin up thanks to SaaS-style products, but holding production-grade quality is a separate problem — what we hear from the field.
A growing number of enterprises are building systems that leverage RAG (Retrieval-Augmented Generation). Through projects with many different customers, ExaWizards has been accumulating know-how on improving accuracy and turning it into business outcomes.
With SaaS-style RAG products such as Dify gaining traction, getting a basic RAG running has become easier. Yet many customers struggle to lift answer quality to the level required for real business use — and to keep it there — relative to the investment in building and operating RAG.
Voices from customers tackling RAG
Construction industry — DX team
"My development team is building a RAG for our business unit. Even after tuning to lift accuracy, we still hear from the business side that ‘answers don't come out well’ and ‘performance feels lower than during testing.'"
Financial services — governance lead
"By the nature of RAG, we add data often, but each addition can break improvements we previously tuned in. We're unsure how to approach this — every component or data change shifts the generated answers, and it feels like a game of whack-a-mole."