QVAi Labs
RAG and Enterprise Knowledge Systems
Retrieval-augmented generation, or RAG, gives a language model relevant source material at the time it answers. The quality of the result depends on the documents, retrieval process, permissions and evaluation—not just the model.
Prepare the knowledge foundation
We examine how documents are collected, updated, indexed and retired. Access rules must carry through to retrieval so a user cannot obtain information they are not authorised to see.
Make answers traceable
A useful knowledge assistant distinguishes supported answers from uncertainty and makes relevant sources available for review. It should handle missing, contradictory or outdated information without presenting a confident invention.
Measure what matters
Evaluation should use real questions and reviewed reference answers. Retrieval relevance, factual support, access-control behaviour, response time and cost need separate checks. Changes to documents, prompts and models should be versioned and retested.
Where this can help
Internal knowledge search, support assistance and document-led workflows are possible starting points. Suitability depends on the quality, sensitivity and ownership of the available information.