Tip #9: The Synonym Trick
Emmanuel Ubiebifaye
Creative Director and Host of Sayit - The Podcast || Co-Founder, Comme Chez Nous
Hey, everyone. I know it's been a minute with us going through all the basics of prompt engineering. I understand that you have probably waited for us to get into real applications like writing CVs, doing detailed research,?and completing term papers.
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So, by next week, our series will be taking a turn into applications. I'll be sharing prompts and dissecting them. We'll also be improving on some prompts we find on the internet and solving some prompting problems together.
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I can't wait to get started, and I hope you're equally excited.
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Let's get on to today's chat.
Today's trick is called the synonyms trick.
HOW IT WORKS
As we've alluded to in the course of this series, iteration is a big part of prompt engineering. Now, because different LLMs (large language models) are trained with different sets of language data, there is no one-size-fits-all prompt for maximum efficiency across the board.
Think of it like social media algorithms. There might be factors that are relevant across the board, but the algorithms operate differently. Why? Because the features and functions of the app, coupled with the audiences tend to create distinct patterns and these algorithms run on pattern recognition.
It's the same thing with these LLM neural networks.
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That being said, your favourite prompt might not be the most efficient on your favourite model. In fact, efficiency is pretty subjective in terms of industry-based applications. A prompt structure might be great for writing articles, but terrible for fact-checking.
On your journey to creating your own prompt libraries, synonyms are a great start at testing out your model's comprehensibility.
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Instead of "generate", you can use "provide".
Instead of "rewrite", you can say "rephrase".
Instead of saying "summarize", you can say "sum up."
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You can try it right now, though they sound the same, they might be interpreted by your chosen model as different things and it's your job as a prompt engineer-in-training to find out what your models are good at and terrible at.
Did you know? ChatGPT - and many language models - are not great at writing large body of texts in reverse.
There you have it, friend. As you continue to iterate your prompts with careful use of synonyms, you'll begin to get a feel for your tool and soon enough, become a pro.
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Now, go practice, and see you tomorrow.
-Your friend, Smiles.