retrieval augmentation

Retrieval augmentation (RAG) is a technique that grounds AI outputs in external, trustworthy data sources to reduce hallucinations and improve factual accuracy. Discussions on WindowsForum highlight RAG's role in curbing AI fabrications, particularly in enterprise deployments like Microsoft Copilot, where licensed content and tenant-specific grounding help mitigate risks. Real-world experiments, such as a public RAG test involving Shell archive materials, demonstrate both the potential and limitations of retrieval augmentation in handling satire, defamation, and tricky prompts. While RAG reduces but does not eliminate hallucinations, it remains a key strategy for organizations seeking reliable AI-assisted workflows.