Exploring a valuable LLM use case: LLM Powered, Reusable, Domain Agnostic Autocompletes
- Time Efficiency: Every second saved during user interaction enhances user satisfaction and directly boosts your earnings potential.
- Reusability Across Domains: These autocompletes can be seamlessly integrated into various tools and applications, regardless of the domain, making them incredibly versatile.
- Adaptive Learning: LLM autocompletes self-improve with each interaction, becoming more accurate and efficient over time.
- Actionable Insights: They provide valuable data about user preferences and needs, which can inform business strategies and product improvements.
- Future-Proof: Staying close to the evolving capabilities of LLMs means that any advancements in the technology will only enhance the functionality of your autocompletes.
yarnyarn dev
cd servercp .env.sample .env(to create server/.env)- Fill in
.env python -m venv venvsource venv/bin/activate(Linux/Mac) orvenv\Scripts\activate(Windows)pip install -r requirements.txtpython main.py
cp .env.sample .env- Fill in
.env yarnyarn ptestyarn view
- There are two
.envto setup..envandserver/.env.