In plain English
Retrieval-Augmented Generation (RAG) is a pattern in which software retrieves relevant information and passes it to a language model alongside the question. The model can then answer using your documents or business records, rather than relying only on its training.
For example, an assistant answering a question about a job can look up the current record. A document assistant can retrieve a relevant contract clause and show the source with its answer. The quality depends on finding the right information, keeping it current and checking the answer. Retrieval does not guarantee that the model will interpret it correctly.
Access controls must apply to retrieval as well as the user interface. A person should only receive information they are allowed to see. Retrieved material is sent to the selected model service as part of the request; where it is processed and retained depends on the architecture and provider settings. RAG does not automatically keep all data on your premises.
Start with a defined set of questions and representative sources. Compare the answers with the originals, test missing or conflicting information, and decide which answers need human review before wider deployment.
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