This post discusses the importance of understanding AI model prompts as akin to traditional software programs, highlighting opportunities and challenges for the programming language and software engineering communities in addressing this perspective.
This article discusses Re2, a prompting technique that enhances reasoning in Large Language Models (LLMs) by re-reading the input twice. It improves understanding and reasoning capabilities, leading to better performance in various benchmarks.
Learn how Google's Prompt Poet simplifies advanced prompt engineering and integrates few-shot learning to rapidly customize LLMs without complex fine-tuning.
Understand temperature, Top-k, Top-p, frequency, and presence penalty for LLM hyperparameters once and for all with visual examples.
Steer LLM outputs towards a certain topic/subject and enhance response capabilities using activation engineering by adding steering vectors, now in oobabooga text generation webui!
This paper surveys different prompt engineering techniques used to improve the performance of large language models on various Natural Language Processing (NLP) tasks. It categorizes these techniques by NLP task, highlights their performance on different datasets, and discusses state-of-the-art methods for specific datasets. The survey covers 44 research papers exploring 39 prompting methods across 29 NLP tasks.
An article on how to properly prompt the Mistral AI Instruct models, explaining the role of BOS, INST, and other special tokens.
How simple prompt engineering can replace custom software
This article by Kory Becker, a software developer, shares insights about prompt engineering, a crucial skill in working with large language models, gained from his AI certification.
An article discussing the current state, recent approaches, and future directions of prompt engineering in data and machine learning. It includes several links to relevant articles and tutorials on the topic.
