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How I Won Singapore’s GPT-4 Prompt Engineering Competition
A deep dive into the strategies I learned for harnessing the power of Large Language Models (LLMs)
Last month, I had the incredible honor of winning Singapore’s first ever GPT-4 Prompt Engineering competition, which brought together over 400 prompt-ly brilliant participants, organised by the Government Technology Agency of Singapore (GovTech).
Prompt engineering is a discipline that blends both art and science — it is as much technical understanding as it is of creativity and strategic thinking. This is a compilation of the prompt engineering strategies I learned along the way, that push any LLM to do exactly what you need and more!
Author’s Note:
In writing this, I sought to steer away from the traditional prompt engineering techniques that have already been extensively discussed and documented online. Instead, my aim is to bring fresh insights that I learned through experimentation, and a different, personal take in understanding and approaching certain techniques. I hope you’ll enjoy reading this piece!
This article covers the following, with 🔵 referring to beginner-friendly prompting techniques while 🔴 refers to advanced strategies: