Q&A: How Google Itself Uses Its Gemini Large Language Model
This year, a great number of announcements came out at Google Next Conference, and perhaps not surprisingly, many were related to new developments around AI.
So we were curious: Were all the AI development tools that Google itself nurtured already helping the company to speed its own development cycle?
At the Las Vegas conference held last week, we asked this question of Paige Bailey, who is Google’s lead product manager for generative AI (GenAI) products, as well as the AI developer relations lead for the company’s Google DeepMind office. DeepMind is the Google AI Lab that created Google Gemini, currently the company’s premier large language model (LLM), used in an array of services from NotebookLM to Code Assist.
It turns out that Gemini and its applications are being used across the board at the company, not only with code completion, but also project planning, documentation and even legacy migration. During the keynote, Alphabet/Google CEO Sundar Pichai noted that already 25% of the code at Alphabet is now generated by AI.
We also spoke about how to get your own company started down the path of using AI. The conversation has been edited for clarity.
How does Google use its Gemini LLM internally?
Gemini is definitely being used across the software development lifecycle at Google.
We use Gemini to help with writing the code itself — Gemini is integrated within the IDE to help create new features, fix things, debug anything that might go wrong. We use it to help with the code review process. We just recently incorporated a GitHub action powered by Code Assist to help review new notebooks and PRs on GitHub whenever they get added.
We use it to help with writing documentation, even though the more tailored documentation still gets a bit more of a human touch. We use it to help with kind of writing some of our overviews of partnerships. We use it for assistance with everything as mundane project management and emails.
Do you find that using AI speeds development of software? Or brings a new depth to the work?
The amount [of acceleration] we’ve seen across the software development lifecycle, I’m not sure we’ve publicly disclosed, but, just personally, I feel like it accelerates my work quite a bit. I’m able to kind of get through a lot of the more mundane code generation things.
I’m able to kind of quickly migrate from one dot release of an API to another, and then I can focus more time on being creative, on actually working with humans, which is the fun part to be honestly, talking with people, helping understand some of their challenges.
There, obviously is a lot of moving parts for the Gemini team itself, and then for all of the new releases that we’re rolling out. But Gemini even helps with summarization of some of that complexity.
Like, as an example, our team has a number of different chat rooms for each new feature that gets released, but we also have a Gemini integration within the chat rooms that can help with summaries. So if you come back six hours later, you can quickly see what are the top items that were discussed, and then if there’s anything actionable that you need to be able to address.
We have a blog post about how Gemini decreases the time to do migrations from one version of an SDK to another. So we’re using it internally. I think the most important piece is asking the engineers how much did this save them, in terms of time and energy. And they found that, for this workstream, 80% of the code modifications were AI-authored. And again, this is just using an older version of Gemini. And then the time spent on the migration was reduced by an estimated 50%. Which is wild. That’s completely unexpected.
So for a product manager in this role, is there a lot of upfront work? When you want to use Gemini or a Gemini-related service, does the project manager have to set up everything at the beginning, so to speak?
We try to make it as easy as possible. There were some features announced in the developer keynote around going from a product requirements document [PRD] to a series of GitHub issues, and then just having Gemini address each one of the issues, so you can see in real time them getting fixed across the Kanban board.
And so for a product manager, like, all you have to do is focus on getting the PRDs right, and then Gemini takes care of turning them into stories, and doing more of that rote work. Honestly, this was one of my least favorite parts when I was a product manager.
Gemini is really great at being able to help get your vision into a state that would be easy for a model to address or a software engineer to address, and it doesn’t require a lot of upfront work at all.
From a software engineering perspective, if you generate an API key for Gemini, all you have to do is go to Cursor or Windsurf or get Copilot, or any of the many open source coding IDEs or extensions like Cline or root code, toss in your Gemini API key, and you’re off to the races.
Would you have any advice for people who do not have a vision? This is for someone who may work for a company that wants AI but is unsure how to start working with it? How do I start working with Gemini?
I know AI is definitely at the top of mind for many different companies today. There’s certainly many people wanting to figure out how to integrate it into their work as quickly as possible.
But I think something that would be really helpful for folks to consider is taking a step back, realizing that you know your business better than anybody else. You know what you’re trying to do better than anyone. You know your users or customers or the people you’re helping better than anyone else.
And so what I like to do when trying to consider how I should integrate AI into my work is what are the parts of the job that are the least joyful and that I wish I could automate the most, whether it’s things like answering emails to schedule meetings or answering emails to just pointing a person towards specific documentation or something, or like, a particular doc, right?
Those are the things that AI can be really, really helpful with, and are very, very simple.
Often I’ll get a really, really long email thread, you know, 35 back-and-forth messages. You can take that thread, give it to Gemini, and then [tell it], ‘I really want to be able to understand this thread. I want to be able to respond to it in this kind of way,’ in a way that it moves along the conversation without getting too much into the detail. Gemini is able to help even with those kinds of more nuanced replies.
We’re all responsible for a broad spectrum of tasks every single day. We’re all whole, complete humans, you know, doing lots of different things. So just identify the bits of your work that are the least joyful that you wish to automate.
How can you improve the accuracy of the results you get from Gemini?
NotebookLM is fantastic. You can add a lot of different cited sources, and then they can point you to precisely where in the document it’s using to give that additional insight. We’ve also incorporated grounding with Google search into our Gemini APIs, if you want to get up-to-date information and fact check sources.
We also have something called Code Execution, which gives you the ability to do some math or plot some data. It gives the ability to Gemini to be able to write code, to run it, and then to recursively fix it if it needs to. So these are great opportunities to give a person additional confidence in the AI model responses, and to help ground them in real data.
For other tech companies out there that want to support Gemini Code Assist, good documentation must be more important than ever…
Absolutely. If the documentation is stale, then that leads to a really bad user experience, so being able to automate that as much as possible is important.
One of the things that’s so challenging for software engineering teams is as you make a new release of your SDK — so maybe you’re going from like a 1.2 to a 1.3 and behaviors might have changed, and maybe you’ve added some new features — it’s really challenging to be able to update every single place in the documentation that touches that old SDK and the new features that are introduced.
Documentation agents are much better at humans than kind of looking through all of those different places and finding all of the instances that need to be upgraded.
As an industry outsider, how should I think about Gemini as a product? I mean there’s all these APIs, applications and plugins that are built off the LLM. But what is Gemini itself?
At DeepMind, we’re investing significant time and energy into making the best models possible. Our most recent Gemini version [version 2.5] incorporates thinking natively, so it has these reasoning characteristics. And it’s multimodal, so it understands video, images, audio, text, code — all of the above — understands these all at once.
But even more remarkably, or at least to me, it can also output different modalities. So you can output text, code, audio, images, and also edit images all with the same model. Historically, you would have needed to have like five different models to do all of those things.
We’re trying to put as much information and capability as possible into one single model, because humans have a lot of different capabilities as well.
So that’s how you can think of the model as this engine that the rest of the application stack can use to do its job.
I will say, though, that the real stickiness and the real innovation comes from this application layer on top. People can build things like Notebook, Cursor, or AI studio. In all of these instances, they integrate the model into the places where users are. This is what really, really makes a difference at the end of the day.
Google is investing a lot in both the model process as well as the application layer, and even things like embodied intelligence.
We’ve just put Gemini on our robotics. We have an open model family, our Gemma open models. So we’re investing not only in proprietary models, but also more kind of community-driven open models.
In addition to the models that we have hosted on servers like our Gemini model family, we’re also taking our small models and integrating them on Pixel devices, with Gemini Nano, as well as embedding them within the Chrome browser.
So instead of having to send data to some server someplace to get a response back, you can do all of that work locally, just using your own local compute. It truly democratizes large language models.
Google paid for the travel expenses for the reporter to attend GCN Next.
