An overview of the Gemini app
James Manyika, SVP, Research, Technology and Society, and Sissie Hsiao, Vice President and General Manager,
Google Assistant and Gemini App
Editor’s note: This is a living document and will be updated periodically as we continue to rapidly improve the Gemini
app’s capabilities as well as address the limitations inherent to LLMs. This overview was last updated on July 25, 2024.
For the latest updates on the Gemini app, visit the Release Updates log or read more on the Google Keyword blog.

We have long seen the potential of AI to make information and computing more accessible and usefulto people. We have made pioneering advances on large language models (LLMs) and have seen great
progress across Google and in this field more broadly. For several years, we have applied LLMs in the
background to improve many of our products, such as autocompleting sentences in Gmail, expanding
Google Translate, and helping us better understand queries in Google Search. We continue using LLMs
for many Google services, as well as to power the Gemini app, which allows people to collaborate
directly with generative AI. We want the Gemini app to be the most helpful and personal AI assistant,giving users direct access to Google’s latest AI models.
While we’re at an important inflection point and encouraged by the widespread excitement aroundgenerative AI, it’s still early days for this technology. This explainer outlines how we’re approaching ourwork on the Gemini app (“Gemini”), including its mobile and web experiences — what it is, how it works
and its current capabilities and limitations. Our approach to building Gemini will evolve as its underlying
technology evolves, and as we learn from ongoing research, experience and user feedback.

What Gemini is
Gemini is an interface to a multimodal LLM (handling text, audio, images and more). Gemini is based on
Google’s cutting-edge research in LLMs, which began with the Word2Vec paper in 2013 that proposednovel model architectures that mapped words as mathematical concepts, followed by the introductionof a neural conversational model in 2015. This framework demonstrated how models could predict thenext sentence in a conversation based on the previous sentence or sentences, leading to more naturalconversational experiences. This was followed by our breakthrough work on Transformer in 2017 andmulti-turn chat capabilities in 2020, which demonstrated even more compelling generative language
progress.
We initially launched Gemini (then called Bard) as an experiment in March 2023 in accordance with our
AI Principles. Since then, users have turned to Gemini to write compelling emails, debug tricky coding
problems, brainstorm ideas for upcoming events, get help learning difficult concepts, and so muchmore. Today, Gemini is a versatile AI tool that can help you in many ways. We already see Gemini helping
people be more productive, more creative, and more curious and we add new functionality andinnovations regularly.
Productivity
For starters, Gemini can save you time. For example, say you are looking to summarize a long researchdocument; Gemini lets you upload it and gives you a useful synthesis. Gemini can also help with coding
tasks, and coding has quickly become one of its most popular applications.
An overview of the Gemini app

1Creativity
Gemini can also help bring your ideas to life and spark your creativity. For example, if you’re writing a
blog post, Gemini can create an outline and generate images that help illustrate your post. And coming
soon with Gems, you will be able to customize Gemini with specific instructions and have it act as a
subject matter expert to help you accomplish your personal goals.
Curiosity
Gemini can be a jumping off point for exploring your ideas and things you’d like to learn more about.
For instance, it can explain a complex concept simply or surface relevant insights on a topic or image.
And soon, it will pair these insights with recommended content from across the web to learn more
about specific topics.
Gemini's capabilities are rapidly expanding -- soon, you’ll be able to point your phone’s camera at an
object, say, for example, the Golden Gate bridge and ask Gemini to tell you about its paint color (ifyou’re wondering, it’s “International Orange”). You’ll also be able to ask Gemini to help you navigate arestaurant’s menu in another language and recommend a dish you’re likely to enjoy. These are just two
examples of the new capabilities coming soon to Gemini.
Of course we rigorously train and monitor Gemini so that its responses are likely to be reliable andaligned with your expectations. We also talk with industry experts, educators, policymakers, businessleaders, civil and human rights leaders, and content creators to explore new applications, risks, andlimitations of this emerging technology.

An overview of the Gemini app

2How Gemini works
Pre-training
Gemini is powered by Google’s most capable AI models, designed with varying capabilities and use
cases. Like most LLMs today, these models are pre-trained on a variety of data from publicly available
sources. We apply quality filters to all datasets, using both heuristic rules and model-based classifiers.
We also perform safety filtering to remove content likely to produce policy-violating outputs. To
maintain the integrity of model evaluations, we search for and remove any evaluation data that may
have been in our training corpus before using data for training. The final data mixtures and weights are
determined through ablations on smaller models. We stage training to alter the mixture composition
during training – increasing the weight of domain-relevant data towards the end of training. Data
quality can be an important factor for highly performing models, and we believe that many interestingquestions remain around finding the optimal dataset distribution for pre-training.
This pre-training allows the model to learn to pick up on patterns in language and use them to predict
the next probable word or words in a sequence. For example, as an LLM learns, it can predict that thenext word in “peanut butter and ___’’ is more likely to be “jelly” than “shoelace.” However, if an LLM picks
only the most probable next word, it will lead to less creative responses. So LLMs are often given
flexibility to pick from reasonable, albeit slightly less probable, choices (say, “banana”) in order togenerate more interesting responses. It’s worth noting that while LLMs can perform well on factual
prompts and create the impression of retrieving information, they are neither information databases
nor deterministic information retrieval systems. So while you can expect a consistent response to a
database query (one that is a literal retrieval of the fixed information stored in the database), an LLM’s
response to the same prompt will not necessarily be the same every time (nor will it literally retrieve theinformation it was trained on). This is also an important reason why LLMs can generateplausible-sounding responses that can at times contain factual errors — not ideal when factuality
matters but potentially useful for generating creative or unexpected outputs.
Post-training
After the initial training, LLMs go through additional steps to refine their responses. One of these iscalled Supervised Fine-Tuning (SFT), which trains the model on carefully selected examples of excellent
answers. It's like teaching children to write by showing them well-written stories and essays.
Next comes Reinforcement Learning from Human Feedback (RLHF). Here, the model learns to generate
even better responses based on scores or feedback from a special Reward Model. This Reward Model istrained on human preference data, where responses have been rated relative to one another, teaching
it what people prefer. Preference data may sometimes include and expose models to offensive or
incorrect data so that they learn how to recognize it and avoid it. You can think of preference data likerewarding a child for a job well done; the model is rewarded for creating answers that people like.
Throughout these stages, it’s important to use high-quality training data. Examples used for SFT aretypically either written by experts or generated by a model and reviewed by experts.
While these techniques are powerful, they have limitations. For example, even with the Reward Model'shelp, a given response might not always be perfect. Still, the LLM is optimized to produce the mostwidely preferred responses based on the feedback it receives, similar to students learning from theirteachers’ comments.

An overview of the Gemini app

3Responses to user prompts
Response generation is similar to how a human might brainstorm different approaches to answering aquestion. Once a user provides a prompt, Gemini uses the post-trained LLM, the context in the prompt
and the interaction with the user to draft several versions of a response. It also relies on externalsources such as Google Search, and/or one of its several extensions, and recently uploaded files(Gemini Advanced only) to generate its responses. This process is known as retrieval augmentation.
Given a prompt, Gemini strives to retrieve the most pertinent information from these external sources(e.g., Google Search) and represent them accurately in its response. Augmenting LLMs with external
tools is an active area of research. There are a number of ways errors can be introduced, including thequery Gemini uses to invoke these external tools, how Gemini interprets the results returned by thetools, and the manner in which these returned results are used to generate the final response. Due tothis, responses generated by Gemini should not reflect on the performance of the individual tools used
to create that response.
Lastly, before the final response is displayed, each potential response undergoes a safety check toensure it adheres to predetermined policy guidelines. This process provides a double-check to filter out
harmful or offensive information. The remaining responses are then ranked based on their quality, withthe highest-scoring version(s) presented back to the user.
We also watermark Gemini text and image outputs using SynthID, our industry-leading digital toolkit forwatermarking AI-generated content. For generated images, SynthID adds a digital watermark (onethat’s imperceptible to the human eye) directly into the pixels. SynthID is an important building block fordeveloping more reliable AI identification tools and can help people make informed decisions about
how they interact with AI-generated content.
Human feedback and evaluation
Even with safety checks, some errors may occur. And Gemini responses may not always fully meet yourexpectations. That's where human feedback comes in. Evaluators assess the quality of responses,
identifying areas for improvement and suggesting solutions. This feedback becomes part of the Gemini
learning process, described in the “Post-training” section above.

Known limitations of LLM-based interfaces like Gemini
Gemini is just one part of our continuing effort to develop LLMs responsibly. Throughout the course ofthis work, we have discovered and discussed several limitations associated with LLMs. Here, we focus
on six areas of continuing research: Accuracy: Gemini’s responses might be inaccurate, especiallywhen it’s asked about complex or factual topics; Bias: Gemini’s responses might reflect biases present
in its training data; Multiple Perspectives: Gemini’s responses might fail to show a range of views;
Persona: Gemini’s responses might incorrectly suggest it has personal opinions or feelings, False
positives and false negatives: Gemini might not respond to some appropriate prompts and provide
inappropriate responses to others, and Vulnerability to adversarial prompting: users will find ways tostress test Gemini with nonsensical prompts or questions rarely asked in the real world. We continue toexplore new approaches and areas for improved performance in each of these areas.

An overview of the Gemini app

4Accuracy
Gemini is grounded in Google’s understanding of authoritative information, and is trained to generate
responses that are relevant to the context of your prompt and in line with what you’re looking for. But
like all LLMs, Gemini can sometimes confidently and convincingly generate responses that containinaccurate or misleading information.
Since LLMs work by predicting the next word or sequences of words, they are not yet fully capable ofdistinguishing between accurate and inaccurate information on their own. We have seen Gemini
present responses that contain or even invent inaccurate information (e.g., misrepresenting how it wastrained or suggesting the name of a book that doesn’t exist). In response we have created features like“double check”, which uses Google Search to find content that helps you assess Gemini’s responses,
and gives you links to sources to help you corroborate the information you get from Gemini.
Bias
Training data, including from publicly available sources, reflects a diversity of perspectives andopinions. We continue to research how to use this data in a way that ensures that an LLM’s response
incorporates a wide range of viewpoints, while minimizing inaccurate overgeneralizations and biases.
Gaps, biases, and overgeneralizations in training data can be reflected in a model’s outputs as it tries topredict likely responses to a prompt. We see these issues manifest in a number of ways (e.g., responses
that reflect only one culture or demographic, reference problematic overgeneralizations, exhibit
gender, religious, or ethnic biases, or promote only one point of view). For some topics, there are data
voids — in other words, not enough reliable information about a given subject for the LLM to learn
about it and then make good predictions — which can result in low-quality or inaccurate responses. We
continue to work with domain experts and a diversity of communities to draw on deep expertise
outside of Google.
Multiple Perspectives
For subjective topics, Gemini is designed to provide users with multiple perspectives if the user does
not request a specific point of view. For example, if prompted for information on something that cannot
be verified by primary source facts or authoritative sources — like a subjective opinion on “best” or“worst” — Gemini should respond in a way that reflects a wide range of viewpoints. But since LLMs like
Gemini train on the content publicly available on the internet, they can reflect positive or negative viewsof specific politicians, celebrities, or other public figures, or even incorporate views on just one side ofcontroversial social or political issues. Gemini should not respond in a way that endorses a particular
viewpoint on these topics, and we will use feedback on these types of responses to train Gemini tobetter address them.
PersonaGemini might at times generate responses that seem to suggest it has opinions or emotions, like loveor sadness, since it has trained on language that people use to reflect the human experience. We havedeveloped a set of guidelines around how Gemini might represent itself (i.e., its persona) and continue
to finetune the model to provide objective responses.

An overview of the Gemini app

5False positives / negatives
We’ve put in place a set of policy guidelines to help train Gemini and avoid generating problematic
responses. Gemini can sometimes misinterpret these guidelines, producing “false positives” and “false
negatives.” In a “false positive,” Gemini might not provide a response to a reasonable prompt,
misinterpreting the prompt as inappropriate; and in a “false negative,” Gemini might generate an
inappropriate response, despite the guidelines in place. Sometimes, the occurrence of false positives orfalse negatives may give the impression that Gemini is biased: For example, a false positive might cause
Gemini to not respond to a question about one side of an issue, while it will respond to the samequestion about the other side. We continue to tune these models to better understand and categorize
inputs and outputs as language, events and society rapidly evolve.
Vulnerability to adversarial prompting
We expect users to test the limits of what Gemini can do and attempt to break its protections, including
trying to get it to divulge its training protocols or other information, or try to get around its safetymechanisms. We have tested and continue to test Gemini rigorously, but we know users will findunique, complex ways to stress-test it further. This is an important part of refining Gemini and we lookforward to learning the new prompts users come up with. Indeed, since Gemini launched in 2023, we’veseen users challenge it with prompts that range from the philosophical to the nonsensical – and insome cases, we’ve seen Gemini respond with answers that are equally nonsensical or not aligned with
our stated approach. Figuring out methods to help Gemini respond to these sorts of prompts is an
on-going challenge and we have continued to expand our internal evaluations and red-teaming to
strive toward continued improvement to accuracy, and objectivity and nuance.

How we’re continuing to develop Gemini
Application of our Gemini approach
Along with our AI Principles, we recently articulated our approach to our work on Gemini: Gemini
should follow your directions, adapt to your needs, and safeguard your experience. Core to ourapproach is a focus on responsibility and safety. Gemini’s policy guidelines seek to avoid certain typesof problematic outputs. We are engaging in ongoing adversarial testing with internal “red team”
members — product experts and social scientists who intentionally stress test a model to probe it foralignment issues with these policy guidelines and our northstar approach for Gemini — so we can apply
what they learn and continuously improve Gemini.
Privacy is also a key consideration as we develop Gemini. The Gemini Apps Privacy Hub has more
information about how we build Gemini with privacy by design, and with you in control.
Enabling user and publisher control
We’ve built a variety of easily accessible Gemini user controls for you to review, update, manage,export, and delete your Gemini data. You can access and review your Gemini prompts, responses, andfeedback through the Gemini Apps Activity control. In addition, you can prevent your future Geminichats from being used to improve Google machine-learning technologies by turning off your Gemini

An overview of the Gemini app

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Apps Activity setting. And like with other Google services, you can also download and export yourinformation through Google’s Takeout tool. We also have controls that allow you to manage public links
you’ve created to your Gemini threads, and controls that allow you to turn on/off access to extensions(e.g., Workspace, Maps, YouTube). We’re also exploring new ways to give you more control overGemini’s responses, including adjusting filters to enable a broader range of responses.
For publishers, we’ve launched Google-Extended, a control that web publishers can use to manage
whether their sites help improve Gemini and Vertex AI generative APIs. Allowing Google-Extended
access to sites’ content can help AI models become more accurate and capable over time. In addition
to not using the content from opted-out URLs for model training, Gemini will also not use such content
for grounding. As AI applications expand, web publishers will face the increasing complexity ofmanaging different uses at scale, and we’re committed to engaging with the web and AI communities
to explore more machine-readable approaches to choice and control.
Improving Gemini together
We believe in rapid iteration and bringing the best of Gemini to the world. User feedback has
accelerated improvements to our models. For example, we use state-of-the-art reinforcement learningtechniques to train our models to be more intuitive and imaginative, and to respond with even morequality and accuracy. We continue to invest in research to learn more about the technical, social, andethical challenges and opportunities of LLMs, both to improve Gemini’s model training and tuning
techniques as well as to share our learnings with researchers, such as this recent paper on the Ethics ofAdvanced AI Assistants. We’re committed to innovating in this space responsibly, collaborating withusers, trusted testers and researchers to find ways for this new technology to benefit the entire
ecosystem.
Transparency is important and we are committed to being open about Gemini's development process
and limitations. Gemini is not a magical black box; it's constantly evolving and we will continue to share
updates on our progress. We’ve launched a Release Updates page so you can see Gemini’s latest
features, improvements, and bug fixes, and we will update this overview as appropriate. We will beidentifying both where Gemini is useful and helpful, and where we need to continue to iterate and makeit better. We are actively adding new capabilities, and through ongoing research, testing, and user
feedback, we look forward to improving Gemini together.
Acknowledgments
We appreciate and acknowledge the incredible work of our colleagues on the Gemini app team, Google
DeepMind, Trust & Safety, and Google Research.

An overview of the Gemini app

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