Retrieval-Augmented Generation (RAG) is a way to make an AI assistant answer using information retrieved from an approved knowledge source instead of relying only on the model's general training.
How RAG works in business terms
Documents and other information are processed into a searchable knowledge layer. When a user asks a question, the system retrieves relevant passages and provides them to the language model as context for the response.
What information can be used?
Depending on the architecture, the knowledge layer can include policies, SOPs, manuals, product documentation, website content, SharePoint files, training material and other approved information.
Where RAG is useful
- Employee policy assistants
- Technical support
- Product and service knowledge
- Document-intensive professional work
- Customer-service assistance
RAG is not the same as “upload a PDF to a chatbot”
Enterprise implementations need ingestion quality, metadata, access control, retrieval design, evaluation and a strategy for keeping information current.
How Cortex Labs can help
Cortex Labs can help assess the workflow, information sources, integrations and technical architecture required to turn this kind of use case into a working solution.
Discuss a relevant use case
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