Retrieval-Augmented Generation (RAG)
Retrieval-augmented generation (RAG) is a technique that lets an AI model pull in relevant information from an external, trusted data source at query time, then generate an answer grounded in that data. It is how businesses make general models specific to their own knowledge.
Instead of relying only on what a model learned in training, RAG retrieves current, owned data, such as customer records or documentation, and feeds it into the response.
RAG makes the quality of your underlying data decisive: grounded, governed first-party data yields trustworthy answers, while fragmented or inaccurate data produces confident mistakes.
Go deeper
12 min readFirst-Party Data and AI: Why Your Models Are Only as Good as Your DataAI agents and models are only as good as the data behind them. Here is why first-party data is the foundation of any serious AI strategy, what AI-ready data looks like, and how to apply it.5 min readWhat Does 'AI-Ready Data' Actually Mean?AI-ready data is owned, unified, accurate, consented, and governed well enough to safely power models and agents. Here is the practical standard, broken down.
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