Mastering Retrieval-Augmented Generation with Gemini API's File Search Tool

I first heard about Retrieval-Augmented Generation (RAG) when I was trying to get an AI agent to provide answers to questions pertaining to internal documents and not necessarily on the data it was trained on. Initially, I imagined it as how one would provide sensitive information only to authorized personnel or users; think company-level kind of access control or NDA-bound information. The goal is to ensure that the AI agent doesn't hallucinate.

In this session, I will be taking the participants through what RAG is and why it's considered as one of the most effective ways to build AI agents that provide better responses based on actual data. I will then take the participants through a codelab on how to do this in an Angular application, powered by Genkit as the AI orchestrator and the new Google File Search tool. File Search is a fully managed RAG system built directly into the Gemini API that abstracts away the retrieval pipeline so that one can focus on building great experiences.