Guidelines for using large language models to improve Python code quality in casual usage.
This paper presents a detailed vocabulary of 33 terms and a taxonomy of 58 LLM prompting techniques, along with guidelines for prompt engineering and a meta-analysis of natural language prefix-prompting, serving as the most comprehensive survey on prompt engineering to date.
A guide on how to understand and read bank statements effectively, highlighting key components and terms, and discussing the importance for financial management and fraud prevention.
The article discusses the challenges people face when using AI, such as treating it like a search engine, and suggests methods to effectively prompt AI, including treating it like a coworker, providing context, and leveraging its infinite patience for task and thought prompting.
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.
