Evaluate product reviews with AI  |  AI on Chrome  |  Chrome for Developers
Skip to main content
Docs
Build with Chrome
Learn how Chrome works, participate in origin trials, and build with Chrome everywhere.
Web Platform
Capabilities
ChromeDriver
Extensions
Chrome Web Store
Chromium
Web on Android
Origin trials
Release notes
Productivity
Create the best experience for your users with the web's best tools.
DevTools
Lighthouse
Chrome UX Report
Accessibility
Modern Web Guidance
Get things done quicker and neater, with our ready-made libraries.
Workbox
Puppeteer
Experience
Design a beautiful and performant web with Chrome.
AI
Performance
CSS and UI
Identity
Payments
Privacy and security
Resources
More from Chrome and Google.
All documentation
Baseline
web.dev
PageSpeed Insights audit
Isolated Web Apps (IWA)
Case studies
Blog
New in Chrome
/
English
Deutsch
Español – América Latina
Français
Indonesia
Italiano
Nederlands
Polski
Português – Brasil
Tiếng Việt
Türkçe
Русский
עברית
العربيّة
فارسی
हिंदी
বাংলা
ภาษาไทย
中文 – 简体
中文 – 繁體
日本語
한국어
Sign in
Docs
AI on Chrome
Built-in AI
Get started
APIs and best practices
Best practices
WebMCP
Learn Evals
Accelerate compute
AI in Chrome
DevTools AI assistance
AI in Extensions
Docs
More
Built-in AI
More
WebMCP
Learn Evals
Accelerate compute
AI in Chrome
More
Case studies
Blog
New in Chrome
Built-in AI
What is built-in AI?
Get started
Benefits of client-side AI
Join the EPP
Try the demos
APIs
API status and overview
Summarizer API
Get started
Summarize in small context windows
Language Detector API
Translator API
Prompt API
Get started
Session management
Session compacting
Structured output
Writer API
Rewriter API
Proofreader API
Polyfill for Prompt API
Polyfill for task APIs
Build with AI
Case studies
Enhance blogging with the Prompt API
Support multilingual experiences
Create engaging article summaries
Create helpful user review summaries
Translate on-device
Client-side or server-side AI
Encourage better reviews with client-side AI
Evaluate reviews with server-side AI
Extensions and AI
Hybrid AI with Firebase AI Logic
Best practices
Cache models
Stream LLM responses
Render streamed LLM responses
Debug the built-in model
Inform users of model download
Understand model management in Chrome
Built-in AI do and don't
Resources
Meet the Chrome team
Glossary and concepts
Gemini API in Node.js
Gemini API in web apps
AI on Web.dev
Build with Chrome
Web Platform
Capabilities
ChromeDriver
Extensions
Chrome Web Store
Chromium
Web on Android
Origin trials
Release notes
Productivity
DevTools
Lighthouse
Chrome UX Report
Accessibility
Modern Web Guidance
Workbox
Puppeteer
Experience
AI
Performance
CSS and UI
Identity
Payments
Privacy and security
Resources
All documentation
Baseline
web.dev
PageSpeed Insights audit
Isolated Web Apps (IWA)
Get started
APIs and best practices
Best practices
DevTools AI assistance
AI in Extensions
Home
Docs
AI on Chrome
Built-in AI
Evaluate product reviews with AI
Stay organized with collections
Save and categorize content based on your preferences.
Maud Nalpas
GitHub
LinkedIn
Bluesky
Kenji Baheux
Alexandra Klepper
GitHub
Bluesky
Published: May 16, 2024
When shopping online, it can be overwhelming to see the volume of product
reviews and the volume of products available. How can we sort through all of
this noise to find the product that will actually meet our specific needs?
For example, say we're shopping for a work backpack. Backpacks need to meet a
balance in function, aesthetics, and practicality. The number of reviews makes
it nearly impossible to know if you have found the perfect bag. What if we could
use AI to sift through the noise and find the perfect product?
What would be helpful is a summary of all reviews, alongside a list of most
common pros and cons.
An example user review with a star rating and a pros and cons list.
To build this, we use server-side generative AI. Inference occurs on a server.
In this document, you can follow along with a tutorial for the
Gemini API with Node.js,
using the Google AI JavaScript SDK to summarize data from many reviews. We focus
on the generative AI portion of this work; we won't cover how to store results
or create a job queue.
In practice, you could use any LLM API with any SDK. However, the
suggested prompt may need to be adapted to meet the model you choose.
Prerequisites
Create a key for the Gemini API,
and define it in your environment file.
Tip: Remember to treat your API keys as secrets: don't check them into source
control. Restrict their scope to your website. Use a separate key for local
development.
Install the Google AI JavaScript SDK, for example with npm:
npm install @google/generative-ai
Build a review summarizer application
Initialize a generative AI object.
Create a function to generate review summaries.
Select the generative AI model. For our use case, we'll use Gemini Pro. Use
a model that's specific to your use case (for example, gemini-pro-vision
is for multimodal input).
Add a prompt.
Call generateContent to pass the prompt as an argument.
Generate and return the response.
const { GoogleGenerativeAI } = require("@google/generative-ai");
// Access the API key env
const genAI = new GoogleGenerativeAI(process.env.API_KEY_GEMINI);
async function generateReviewSummary(reviews) {
// Use gemini-pro model for text-only input
const model = genAI.getGenerativeModel({ model: "gemini-pro" });
// Shortened for legibility. See "Write an effective prompt" for
// writing an actual production-ready prompt.
const prompt = `Summarize the following product reviews:\n\n${reviews}`;
const result = await model.generateContent(prompt);
const response = await result.response;
const summary = response.text();
return summary;
}
Caution: Reviews come (mostly) from real humans and thus are often attached to
information which could be considered personally-identifying information (PII).
PII isn't useful for summarization tasks and may negatively impact the quality.
Always remove any potential PII before sharing data with any AI model.
Write an effective prompt
The best way to be successful with generative AI is to create a thorough prompt.
In this example, we've used the one-shot prompting technique to get
consistent outputs.
Tip: Consider using Google AI Studio for prompt
engineering and iteration.
One-shot prompting is represented by the example output for Gemini to model.
const prompt =
`I will give you user reviews for a product. Generate a short summary of the
reviews, with focus on the common positive and negative aspects across all of
the reviews. Use the exact same output format as in the example (list of
positive highlights, list of negative aspects, summary). In the summary,
address the potential buyer with second person ("you", "be aware").
Input (list of reviews):
// ... example
Output (summary of reviews):
// ... example
**Positive highlights**
// ... example
**Negative aspects**
// ... example
**Summary**
// ... example
Input (list of reviews):
${reviews}
Output (summary of all input reviews):`;
Here's an example output from this prompt, which includes a summary of all
reviews, alongside a list of common pros and cons.
## Summary of Reviews:
**Positive highlights:**
* **Style:** Several reviewers appreciate the backpack's color and design.
* **Organization:** Some users love the compartments and find them useful for
organization.
* **Travel & School:** The backpack seems suitable for both travel and school
use, being lightweight and able to hold necessary items.
**Negative aspects:**
* **Durability:** Concerns regarding the zipper breaking and water bottle holder
ripping raise questions about the backpack's overall durability.
* **Size:** A few reviewers found the backpack smaller than expected.
* **Material:** One user felt the material was cheap and expressed concern about
its longevity.
**Summary:**
This backpack seems to be stylish and appreciated for its organization and
suitability for travel and school. However, you should be aware of potential
durability issues with the zippers and water bottle holder. Some users also
found the backpack smaller than anticipated and expressed concerns about the
material's quality.
Token limits
Many reviews can hit the model's token limit. Tokens aren't always equal to a
single word; a token can be parts of a word or multiple words together. For
example, Gemini Pro has
a 30,720 token limit. This means the prompt can include, at-most, 600 average
30-word reviews in English, minus the rest of the prompt instructions.
Use countTokens()
to check the number of tokens and reduce the input if the prompt is larger than
allowed.
const MAX_INPUT_TOKENS = 30720
const { totalTokens } = await model.countTokens(prompt);
if (totalTokens > MAX_INPUT_TOKENS) {
// Shorten the prompt.
}
Build for enterprise
If you're a Google Cloud user or otherwise need enterprise support, you can
access Gemini Pro and more models, such as Anthropic's Claude models, with
Gemini Enterprise Agent Platform. You may want to use
Model Garden to determine which model
best matches your specific use case.
Next steps
The application we built relies heavily on quality reviews to give the most
effective summaries. To collect those quality reviews, read the next article in
this series is
Help users write useful product reviews with on-device web AI.
We want to hear from you about this approach. Tell us what use cases most
interest you. You can
share your feedback and join the Early Preview Program
to test this technology with local prototypes.
Your contribution can help us make AI a powerful, yet practical, tool for
everyone.
Next: Help users
write useful product reviews
Except as otherwise noted, the content of this page is licensed under the Creative Commons Attribution 4.0 License, and code samples are licensed under the Apache 2.0 License. For details, see the Google Developers Site Policies. Java is a registered trademark of Oracle and/or its affiliates.
Last updated 2024-05-16 UTC.
[[["Easy to understand","easyToUnderstand","thumb-up"],["Solved my problem","solvedMyProblem","thumb-up"],["Other","otherUp","thumb-up"]],[["Missing the information I need","missingTheInformationINeed","thumb-down"],["Too complicated / too many steps","tooComplicatedTooManySteps","thumb-down"],["Out of date","outOfDate","thumb-down"],["Samples / code issue","samplesCodeIssue","thumb-down"],["Other","otherDown","thumb-down"]],["Last updated 2024-05-16 UTC."],[],[]]
Contribute
File a bug
See open issues
Related content
Chromium updates
Case studies
Archive
Podcasts & shows
Follow
@ChromiumDev on X
YouTube
Chrome for Developers on LinkedIn
RSS
Terms
Privacy
Manage cookies
English
Deutsch
Español – América Latina
Français
Indonesia
Italiano
Nederlands
Polski
Português – Brasil
Tiếng Việt
Türkçe
Русский
עברית
العربيّة
فارسی
हिंदी
বাংলা
ภาษาไทย
中文 – 简体
中文 – 繁體
日本語
한국어