Meta introduced "Prompt Engineering with Llama 2", an interactive Jupyter Notebook guide for developers, researchers, and enthusiasts working with large language models (LLMs). The guide covers prompt engineering techniques, best practices, and showcases various prompting methods such as explicit instructions, stylization, formatting, restrictions, zero- and few-shot learning, role prompting, chain-of-thought, self-consistency, retrieval-augmented generation, and program-aided language models. The guide also demonstrates how to limit extraneous tokens in LLM outputs by combining roles, rules, explicit instructions, and examples. The resource aims to help users achieve better results with LLMs by effectively using these techniques. The Jupyter notebook is available from the llama-recipes repository.

Support our independent, free-access reporting. Any contribution helps and secures our future. Support now:
Bank transfer
Online journalist Matthias is the co-founder and publisher of THE DECODER. He believes that artificial intelligence will fundamentally change the relationship between humans and computers.
Join our community
Join the DECODER community on Discord, Reddit or Twitter - we can't wait to meet you.