Introducing ๐ฆ๐๐ฝ๐ฒ๐ฟ ๐๐ฆ๐ข๐ก ๐ ๐ผ๐ฑ๐ฒ, a framework for low latency structured output generation from LLMs.
Generate JSON up to ๐ฎ๐ฌ๐ ๐ณ๐ฎ๐๐๐ฒ๐ฟ from OpenAI and open source models.
โ No need to threaten the model, tip the AI, etc โ
Built with @derhacobian ๐ง
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1/ Suppose we want to extract the following characteristics from a house listing using an LLM:
- `address`
- `square footage`
- `number of bedrooms`
- `number of bathrooms`
- `price`
We could naively prompt the model to fill in this schema according to the description.
Apr 18, 2023 โข 6 tweets โข 3 min read
I connected ChatGPT to my personal health data on my iPhone.
Now, I can have a conversation with my digital health history.
The code is also public.
Say hello to HealthGPT ๐
I used it earlier this week to check in on my weekly gym goals.
Imagine a future where everyone has an personalized AI health assistant that can:
- track your data in a privacy-first manner
- understand your body's unique needs
- offer tailored advice towards your fitness goals
Feb 13, 2023 โข 10 tweets โข 2 min read
A short guide to engineering better GPT-3 prompts.
Here's how I redesigned GraphGPT's prompt to become faster, more efficient, and handle larger inputs:
Some quick background:
GraphGPT is a tool that generates knowledge graphs from unstructured text using OpenAI's GPT-3 large language model.