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How to optimize prompt engineering for large language models (LLMs)? Optimizing prompt engineering for large language models (LLMs) is an iterative process focusing on refining prompts to maximize output relevance, coherence, and alignment with desired outcomes. Here’s a… https://t.co/HNT2V88Qtq
LLM Prompt Tuning Playbook By @VarunGodbole and Ellie Pavlick Summary This document, written by researchers Varun Godbole and Ellie Pavlick, focuses on prompt tuning for Large Language Models (LLMs). It aims to provide both mental models and practical techniques to improve… https://t.co/MOQv4lfoqi https://t.co/tlmatNgeIB
LLMs are a nice way to practice communicating with humans. If you can't create a prompt that an LLM can mostly understand, you'll have a hard time communicating the idea with another human too.
Google DeepMind has open-sourced its internal prompt-tuning guide, providing detailed descriptions of pretraining and post-training processes, system instructions, and best practices for optimizing prompts for large language models (LLMs). The guide, authored by researchers Varun Godbole and Ellie Pavlick, aims to improve the effectiveness of LLMs through refined prompt engineering and includes mental models. Prompt engineering has become an essential skill for small businesses, with professionals in the field earning between $120,000 and $300,000 annually. The release of this guide highlights the growing importance of prompt engineering in leveraging AI capabilities.
