Celebrating a milestone — The real win was the priceless learning experience!

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:

1. [🔵] Structuring prompts using the CO-STAR framework

2. [🔵] Sectioning prompts using delimiters

3. [🔴] Creating system prompts with LLM guardrails

4. [🔴] Analyzing datasets using only LLMs, without plugins or code —
With a hands-on example of analyzing a real-world Kaggle dataset using GPT-4

1. [🔵] Structuring Prompts using the CO-STAR framework

Effective prompt structuring is crucial for eliciting optimal responses from an LLM. The CO-STAR framework, a brainchild of GovTech Singapore’s Data Science & AI team, is a handy template for structuring prompts. It considers all the key aspects that influence the effectiveness and relevance of an LLM’s response, leading to more optimal responses.