RAG: LLMs with your data

Opening stream of the Hackathon to onboard you in the Hackathon rules, technology and guide through your Hackathon journey.

Hackathon Kick-Off

Please note, the time of the session may be changed. Follow the announcements via email and in the Discord channel.

Vectara Workshop: learn the technology and get started with your project!

Anyscale Introduction

Join us and experience the excitement as you actively engage in our dynamic panel discussion on RAG. This captivating discussion will be expertly moderated by Ofer Mendelevitch from Vectara. 

RAG Panel Discussion

General Q&A session on Discord

Join the Talk to improve your applications UI!

The importance of UI for developing RAG applications

Join Forrest Bao and Ofer Mendelevitch for the discussion of the latest Vectara's release and make the most of it for your Hackathon submission!

Vectara New Feature Announcement For Hackathon Participants

Hackathon Submission Deadline

LEGALLY LITT: Legal Insight through Technology Transformation

LITT

lowcy

Leverage the power of Large Language Model Vectara to automate and streamline the patent litigation workflow by automating text generation in your patent applications for prior art search, patent drafting, patent analysis, and subject matter categorization. 

The platform aims to save time, reduce costs, and improve the overall effectiveness of patent litigation processes built by integrating cutting-edge technologies such as Vectara, a large language model, with patent-specific datasets obtained from Google Patents. Improve accuracy by reducing errors caused by manual entry. Minor inconsistencies can lead to costly litigation-  minimizing trivial errors, increasing technical accuracy and focusing on the patent application quality reducing poor patent decisions in the absence of patent analytics. 

Avoid costly mistakes down the line with filing patents. Cost-effective reviews of their patent applications and analyze them in minutes. Time-savings and improved accuracy translate to direct savings and increasing accessibility of patents in the process.

Generate text based on patent claims with RAG and grounded generation to eliminate hallucinations with a text corpus from Google Patents. Creating a system for generating text based on patent claims using RAG and grounded generation, coupled with a text corpus from Google Patents, can significantly improve the quality and reliability of generated content. By incorporating grounding techniques, such as fact-checking against a reputable corpus, you can mitigate hallucinations and ensure that the generated text is based on factual information. The retrieval component can use the structured corpus data to find relevant information related to a specific patent claim. Implement fact-checking mechanisms to validate the accuracy of the generated text. If the generated content contradicts the corpus data, the system should flag or modify it.

Cheaper and faster first-to-file patents with LEGALLY LITT. Our "Legal Insight through Technology Transformation" leverages RAG with Vectara in the Legal Domain. Grounded generation to eliminate hallucinations with a text corpus from Google Patents.

"LITT team on RAG: LLMs with your data Hackathon"

Team Idea

Submission