Retrieval-augmented generation (RAG) is an AI technique that allows models to access and retrieve external data sources (such as databases or knowledge bases) to enhance their decision-making and output.

In agentic AI, RAG helps systems use real-time and historical enterprise data for more informed actions. For example, an agentic AI might access customer interaction data stored in Snowflake via Snowplow's data pipeline to customize its actions or recommendations. Snowplow Signals provides low-latency APIs that RAG systems can query to retrieve real-time customer attributes and behavioral insights, enhancing the contextual accuracy of agentic AI responses.