A company wants its new internal chatbot to answer gcp video

The correct option is Managed RAG in Vertex AI Search. This service provides a managed retrieval augmented generation workflow that indexes your private content and retrieves the most relevant passages so the model generates answers grounded in those sources with citations.

With Managed RAG, you create data stores and use connectors in Vertex AI Search to ingest documents from locations like Cloud Storage or enterprise repositories. At query time Vertex AI Search retrieves the top passages and feeds them to the model so the response stays anchored to your documents and can include links back to the source. You do not need to build custom indexing, retrieval, and orchestration because Vertex AI Search handles that for you.

Cloud Search is an enterprise search product for Google Workspace and other sources and it returns search results but it does not provide a managed retrieval augmented generation flow that grounds a generative model and produces cited answers.

Document AI focuses on document understanding and extraction such as OCR and structured field parsing. It is not a retrieval augmented generation service and it does not ground a chat response on retrieved passages with citations.

Fine tune a foundation model on the policy corpus changes model parameters to better learn patterns from your data, yet it does not implement retrieval from up to date sources and it does not ensure answers cite documents. Fine tuning is not the right choice when the goal is grounded answers from private documents.

When you see a requirement for answers to come from private documents with citations think about managed RAG in Vertex AI Search rather than extraction tools or model fine tuning.