How I Gained Singapore’s GPT-4 Immediate Engineering Competitors | by Sheila Teo | Dec, 2023


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A deep dive into the methods and takeaways I realized alongside the best way

Sheila Teo

Towards Data Science
Celebrating a milestone — The actual win was the priceless studying expertise!

Final month, I had the unimaginable honor of profitable Singapore’s first ever GPT-4 Immediate Engineering competitors, which introduced collectively over 400 prompt-ly sensible contributors, organised by the Authorities Expertise Company of Singapore (GovTech).

Immediate engineering is a self-discipline that blends each artwork and science — it’s as a lot technical understanding as it’s of creativity and strategic pondering. This text is a compilation of the immediate engineering methods and insights that I realized alongside the best way, that push any LLM to do precisely what you want and extra!

This text covers the next, with 🟢 referring to beginner-friendly prompting strategies whereas 🟠 refers to superior methods:

1. [🟢] Structuring prompts utilizing the CO-STAR framework

2. [🟢] Sectioning prompts utilizing delimiters

3. [🟠] Utilizing system prompts with LLM guardrails

4. [🟠] Analyzing datasets utilizing solely LLMs, with out plugins or code

Efficient immediate structuring is essential for eliciting optimum responses from an LLM. The CO-STAR framework, a brainchild of GovTech Singapore’s Information Science & AI workforce, is a helpful template for structuring prompts. It considers all the important thing points that affect the effectiveness and relevance of an LLM’s response, resulting in extra optimum responses.

CO-STAR framework — Picture by creator

Right here’s the way it works:

(C) Context: Present background data on the duty.

This helps the LLM perceive the particular situation being mentioned, guaranteeing its response is related and aligned together with your expectations.

(O) Goal: Outline what the duty is that you really want the LLM to carry out.



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