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.

• yarn
• yarn dev

• cd server
• cp .env.sample .env (to create server/.env)
• Fill in .env
• python -m venv venv
• source venv/bin/activate (Linux/Mac) or venv\Scripts\activate (Windows)
• pip install -r requirements.txt
• python main.py

• cp .env.sample .env
• Fill in .env
• yarn
• yarn ptest
• yarn view

• There are two .env to setup. .env and server/.env.

• Live Demo
• Improve your prompt chains
• Evaluate Your Prompts
• Promptfoo
• Groq