Retrieval augmented generation, or RAG, has become a foundational approach to building

production AI systems. However, deploying RAG in practice can be complex and costly. Developers

typically have to manage vector databases, chunking strategies, embedding models, and indexing infrastructure.

Designing effective RAG systems is also a moving target,

as techniques and best practices evolve in step with rapidly advancing language models.

Google DeepMine recently released the File Search tool,

a fully managed rag system built directly into the Gemini API.

File search abstracts away the retrieval pipeline, allowing developers to upload documents, code, and other text data,

automatically generate embeddings, and query their knowledge base.

We wanted to understand how the DeepMind team designed a general-purpose rag system that maintains high retrieval quality.

Anemesh Chatterjit is a software engineer at Google DeepMind, and Yvonne Salivyev is a product

manager at DeepMind, and they worked on the file search tool. They joined the podcast with

Sean Falconer to discuss the evolution of RAG, why simplicity and pricing transparency

matter, how embedding models have improved retrieval quality, the tradeoffs between

configurability and ease of use, and what's next for multimodal retrieval across text,

images, and beyond. This episode is hosted by Sean Falconer.

Check the show notes for more information on Sean's work and where to find him.

Yvonne and Anamesh, welcome to the show.

Hi, Sean.

I'm proud to be here.

Awesome.

Well, why don't we, you know, we have two guests today just so everyone can kind of learn whose voice is who.

...