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. For more information about the layout parser, see Use the layout parser.
- 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.
Ground responses using Vertex AI RAG Engine
To ground responses using Vertex AI RAG Engine, you must create a prompt. Do the following:
In the Google Cloud console, go to the Create prompt page using Vertex AI Studio.
Select Grounding: Your data.
Select RAG Engine grounding source.
From the Corpus list, select your corpus name.
In the Top-K Similarity field, select 20, which is the default.
Click Save.
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 RAG Engine, see
RAG Engine.