What is retrieval-augmented generation (RAG)?
Lets AI first look up relevant content in the company's own materials and answer on that basis, reducing off-target and made-up answers.
In detail
Large language models do not know a company's internal materials. RAG works like this: first, policies, product manuals, contracts and other documents are organized into a searchable knowledge base; when someone asks a question, the system finds the most relevant passages and passes them to the model, so it answers “with the materials in front of it” and can cite its sources.
For example
A salesperson asks, “How long is the warranty on this product?” The system finds the warranty clause in the product manual, and the model answers “24 months”, citing the relevant section of the manual.
How companies use it
Commonly used for internal knowledge Q&A, customer service assistants, and policy and process lookups. How well it works depends largely on whether the materials themselves are accurate, complete and kept up to date.
Common misconceptions
Thinking you can just dump all your files in. If the materials are outdated, contradictory or poorly formatted, the AI's answers will be wrong too.
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