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> Over the years I’ve utilized all the tricks to get the best results out of LLMs.

The poster tips their hand early in the article and I can see there won't be much substance here. I work on writing prompts for production solutions that use LLMs to QA various text inputs that would be very hard to do using traditional NLP techniques. Good prompt engineering had very little to do with thinking up ridiculous scenarios to "trick" the LLM into being better. Those are actually counterproductive because their efficacy can vary widely across model versions.



Can you give a concise example of good prompt engineering in your case?




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