Try Gemini 1.5 models, the latest multimodal models in Vertex AI, and see what you can build with up to a 2M token context window. Try Gemini 1.5 models, the latest multimodal models in Vertex AI, and see what you can build with up to a 2M token context window.

Ground responses using RAG

Grounding is a technique that you can use to help produce model responses that are more trustworthy, helpful, and factual. When you ground generative AI model responses, you connect them to verifiable sources of information. To implement grounding, usually, you must retrieve relevant source data. The recommended best practice is to use the retrieval-augmented generation (RAG) technique. Retrieval is usually done using a search engine, which uses an index that's embedded with the semantic meanings of the source text.

There are also services and component APIs that implement the RAG lifecycle, such as the Vertex AI Search Builder API, which allows for mix-and-match building. With mix-and-match building, you can implement a RAG solution using any of the following services or APIs:

Grounding generation API: You can use it to implement grounding, or link to a retrieval provider for the complete RAG lifecycle.
Document layout parser: This parser represents the best of Document AI and Gemini for document understanding.
Vertex AI Vector Search: This search service is highly performant and uses a high-quality vector database.
Check grounding API: This API compares RAG output with the retrieved facts and helps to ensure that all statements are grounded before returning the response to the user.

What's next

To learn more about responsible AI and safety filters, see responsible AI best practices and Vertex AI's safety filters.
To learn more about how RAG is implemented by LlamaIndex on Vertex AI for RAG, see LlamaIndex on Vertex AI for RAG.