- Deeplearning.ai ChatGPT Prompt Engineering for Developers
- Prompt Engineering Guide
- Pinecone LangChain AI Handbook
LMQL: Programming Large Language Models: “LMQL is a programming language for language model interaction.”
LMQL generalizes natural language prompting, making it more expressive while remaining accessible. For this, LMQL builds on top of Python, allowing users to express natural language prompts that also contain code. The resulting queries can be directly executed on language models like OpenAI’s GPT models Fixed answer templates and intermediate instructions allow the user to steer the LLM’s reasoning process.
This is the thing I am most deeply interested in theoretically
To analyze the effect of GPT-4 on labor efficiency and the optimal mix of capital to labor for workers who are good at using GPT versus those who aren’t when it comes to performing cognitively costly tasks, we will consider the Goldin and Katz modified Cobb-Douglas production function
More at LLM economics
Information search matters
Running those big cloud models on the edge, i.e. locally.
- Could you train a ChatGPT-beating model for $85,000 and run it in a browser?
- Large language models are having their Stable Diffusion moment
- Stanford Alpaca, and the acceleration of on-device large language model development
- Transformers.js Run 🤗 Transformers in your browser!