Meta publishes prompt engineering guide for Llama 2


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.

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