# Project export: Levi's Word Leap

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## Project metadata

- Hackathon: TreeHacks 2024
- Tagline: Improve your vocabulary with Levi's Word Leap! Obtain tailored feedback on your vocabulary with AI. We express ourselves through words, so let's learn new words today to better connect with others.
- Devpost: https://devpost.com/software/levi-s-word-leap
- GitHub: not linked
- Team: contributor stats unavailable

## Devpost submission (written by the team)

### Inspiration

Words are the primary medium through which we express ourselves. It's clear that many people are interested in expanding their familiarity with new words; after all, that is why millions of people play Wordle and hundreds of thousands also follow "Word of the Day" newsletters. We must continue to develop methods that help people build their vocabulary, for it helps people communicate effectively and descriptively. While the aforementioned online word games are effective tools to supporting people's ability to access vocabulary resources, tools which suggest new terms in a personalized and at a higher cadence would help people even more. "Levi's Word Leap" has people converse with an AI in a gamified setting. The vocabulary suggestions are derived from the person's conversation, thus naturally making these suggestions tailored to each individual. People can talk to Levi at any time, and, as an added benefit, they can obtain vocabulary suggestions in the context of real conversations.

### What it does

Levi's Word Leap has two parts. First, is an AI-driven conversation. A person can chat with the AI, and the AI will respond in an engaging manner. Second, Levi analyzes this conversation against an English dictionary containing 100,000+ words, and identifies key terms that the person can use without significantly changing the meaning of their statements. Levi's Word Leap gives each conversation a score, thus allowing friends to compete with each other, a motivating factor for many.

### How we built it

We use Together.AI's embeddings to embed an English dictionary into a ChromaDB database. We use the Mixtral LLM to generate conversations and analyze conversations for suggested vocabulary terms. The frontend is built using Reflex and the backend is implemented in Flask.

### Challenges we ran into

It was very difficult implementing retrieval augmented generation on such a large corpus. We also faced issues with the prompt engineering.

### Accomplishments we're proud of

Our app works!

### What we learned

RAG, AI, LLMs, Reflex

### What's next

Voice chat!

## README (from the GitHub repository)

No README available.

## Detected evidence (automated analysis)

No repository was indexed for this project. Claimed technologies below could not be checked against code.
- OpenAI (technology) — claimed on Devpost, not found in the code

## Codebase structure

No repository index available.

## Key source files

No repository index available; no source files included.