Developers spend 35% of their time just understanding how to use APIs. What if your API documentation could just talk when asked: “How do I authenticate?” and provide a clear, grounded answer with code snippets? This workshop explores how to move beyond static API documentation and create an interactive AI Documentation Agent that developers can query directly. Using Retrieval-Augmented Generation (RAG), Google’s Gemini models, and Vertex AI Search, we’ll walk through how to ingest API docs, ground them in a vector database, and serve reliable answers through a conversational interface.

By the end of this session, aattendees will be able to:
* Design a RAG Pipeline for API Docs: Understand how to apply the RAG framework specifically to technical documentation, including how to handle structured and unstructured data like OpenAPI specifications, Markdown, and code examples.

* Build a Knowledge Base with Vertex AI Search: Implement a pipeline to ingest and index API documentation using Vertex AI Search, turning raw content into a powerful, semantic-searchable knowledge base.

* Generate Contextual Answers with Gemini: Connect the retrieved context with the Gemini API to construct intelligent prompts and generate accurate responses that are directly relevant to a developer's query.

Key takeaways
Attendees will leave this session with:
* A step-by-step guide for building their own AI documentation agent, complete with code examples and a live demo to serve as a practical reference.
* The knowledge to implement a reliable AI solution that can be applied to other use cases beyond API documentation, such as internal knowledge bases or customer support.