Grounding overview

In generative AI, grounding is the ability to connect model output to verifiable sources of information. When you provide models with access to specific data sources, grounding connects their output to this data and reduces the chances of the model inventing content. This is particularly important in situations where accuracy and reliability are significant.

Grounding provides the following benefits:

  • Reduces model hallucinations: Grounding helps prevent instances where the model generates content that isn't factual.
  • Anchors model responses: It helps ensure that model responses are based on your specific data sources.
  • Enhances auditability: Grounding provides links to the sources used, which allows for verification.

You can ground a supported model's output in Vertex AI in the following ways:

Grounding type Description
Grounding with Google Search You want to connect your model to world knowledge and a wide possible range of topics.
Grounding with Google Maps You want to use Google Maps data with your model to provide more accurate and context-aware responses to your prompts.
Grounding Gemini to your data You want to use retrieval-augmented generation (RAG) to connect your model to your website data or your sets of documents.
Grounding Gemini with Elasticsearch You want to use retrieval-augmented generation with your existing Elasticsearch indexes and Gemini.
Web Grounding for Enterprise You want to use a web index to generate grounded responses.

For language support, see Supported languages for prompts.

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