What is Retrieval-Augmented Generation
A pattern where a generative model retrieves external information before producing an answer.
Definition
Retrieval-Augmented Generation is a pattern where a generative model retrieves external information before producing an answer. In practical AI work, it helps teams connect a concept to data, model behavior, product choices, evaluation, and risk. The useful question is not only what the term means, but how it affects quality, cost, reliability, and decisions in a real workflow.
Example
A legal assistant retrieves passages from approved documents before drafting an answer with citations.
Why it matters
Retrieval-Augmented Generation matters because a pattern where a generative model retrieves external information before producing an answer can change how teams build, evaluate, choose, or govern AI systems. It affects cost, reliability, latency, security, and how easily an AI feature can move from a demo to production.
How it works
Teams connect data, compute, model artifacts, libraries, monitoring, access control, and deployment tools into a repeatable workflow. For Retrieval-Augmented Generation, the key is to connect the definition with inputs, assumptions, measurable outcomes, and deployment limits.
Where it is used
- Used in model training, inference, data processing, deployment, evaluation, monitoring, and developer tooling.
Limitations
Infrastructure choices can lock teams into particular costs, vendors, latency profiles, or operational constraints.