Retrieval Augmented Generation anchors the AI's responses to your own documents, data, or knowledge base instead of letting it draw only from its training. This closes the gap between generic answers and expert answers by giving the AI access to your specific context—your project details, company knowledge, or proprietary information—so it can reason from fact rather than assumption.
Retrieval Augmented Generation, or RAG, is a technique where AI answers are grounded in a specific set of documents or data sources you provide, rather than relying solely on its pre-trained knowledge.
Understanding RAG helps you recognize when and how to supply AI with your own files, notes, or records so that responses are accurate, relevant, and based on information the model would not otherwise have access to.
Peri can explain this concept, give practical examples, help you decide whether it applies to your situation, or recommend a journey if appropriate.
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