Fwd: How I Prompted My Way to Publish-Ready Content - kaustubhs@groww.in - Groww Mail

#3—Be methodical about the examples you provide The staple of any few-shot prompt is its examples. I had strict screening criteria for the examples that were added to my contrarian, punchy, tweet Spiral. The only tweets that were allowed were those that: did not exceed the single-tweet word count (i.e., were not a thread or long post) had over 100,

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One reason that helps to explain why Lucas’s scrappy team was able to snatch victory from the jaws of defeat is because they were in total alignment. Prior to the shoot, Lucas sat them down and had them watch four films that embodied his desired vibe for Star Wars: 2001, Silent Running, Once Upon a Time In the West, and Satyricon. Armed with these

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#1—Less is often more when it comes to instructions For whatever reason, the presence of certain words can skew LLMs into unexpected behaviors. Additional instructions will often go ignored until you remove the offending words or instructions. Recent research shows that a less-is-more approach seems to work for LLMs on a pretty profound level. For

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#4—Prompting is a finicky art, but a worthy investment While it would be great if the three techniques I’ve shared so far immediately transform your prompts, there's a decent chance they will fall flat for your use case. My guess is that they will even become irrelevant as models become smarter and more cost-effective over time. My point in sharing

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Overcoming few-shot friction There is one problem I’ve noticed with example-based prompting: Most creators and businesses aren’t using it. I think this is because you have to overcome three major inconveniences before you start to take advantage of few-shot prompting on a consistent basis: Upfront effort: A few-shot prompt is only as good as the ex

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