Retrieval-Augmented Generation (RAG)
Stage 3 addresses a misconception that repeatedly causes enterprise AI deployments to fail. Many organisations assume that accuracy is mainly a function of model size. They believe that selecting a larger model automatically produces more reliable outputs. Larger models often produce more fluent language and can perform better on complex reasoning tasks, yet fluency and reasoning strength do not guarantee accuracy about your organisation. Accuracy in enterprise work is primarily evidence-dependent. It depends on whether the system has access to the correct internal sources and whether it is designed to use those sources in a disciplined way.
This module introduces retrieval-augmented generation, commonly abbreviated as RAG. RAG is a professional reliability method used to enforce grounding. Grounding means anchoring outputs to verifiable organisational evidence. In environments governed by policies, contracts, and compliance obligations, grounding is not an optional enhancement. It is a necessary control mechanism. It reduces the risk of outputs that sound credible but are not supported by the organisation’s actual rules.
Stage 3 treats RAG as part of operational governance. Teams learn how to design workflows where the model behaves like an analyst who cites internal documents, rather than a general assistant that speaks from pattern memory.