Quilt is a newly launched Retrieval-Augmented Generation UI that enhances document interaction with features like multi-user setup, advanced search capabilities, and customizable settings, and invites user feedback on additional features.
JavaScript, Python, likely utilizing machine learning models for RAG functionality.
2024-10-02
- website
Highlight 1
The multi-user setup allows for collaboration, making it ideal for teams or shared projects.
Highlight 2
The hybrid RAG pipeline effectively combines full-text and vector searches, improving the reliability and relevance of search results.
Highlight 3
The smart citation feature enhances academic and professional document use by providing in-browser previews and highlights for easier reference.
Improvement 1
Consider enhancing the onboarding process to better guide new users in utilizing all features effectively.
Improvement 2
Improving the mobile responsiveness of the web UI to ensure a seamless experience across different devices.
Improvement 3
Implementing more advanced personalization options to further customize the interface and user workflows.
Product Functionality
Consider adding advanced analytics to monitor user engagement with documents and search effectiveness.
UI & UX
Incorporate clearer navigation elements and tooltips to assist users unfamiliar with RAG technology.
SEO or Marketing
Implement SEO strategies by optimizing content for search engines, including targeted keywords and enhancing page loading times.
MultiLanguage Support
Introduce multi-language support to cater to an international audience, making it accessible to non-English speakers.
- 1
Is creating an account mandatory?
Yes, creating an account is free but necessary to ensure that your documents remain separate and secure.
- 2
Can I collaborate with others on my document collections?
Absolutely! Quilt supports a multi-user setup that allows you to share collections with others, both publicly and privately.
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What types of searches does Quilt support?
Quilt utilizes an advanced hybrid RAG pipeline that combines both full-text and vector searches for more effective document retrieval.