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49 lines
2.7 KiB
Markdown
49 lines
2.7 KiB
Markdown
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# Text writing examples
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Large language models are excellent at writing. They can assist with all sorts of tasks:
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* Blog posts
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* Email copy
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* Ad copy
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* Website copy
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* Product descriptions
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* Memos
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* Storytelling
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* Brainstorming
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* Question generation
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* etc.
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The easiest way to prompt GPT-3 is to tell it what you'd like. For example, if you want it to write an email, you could use a prompt like this:
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```text
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Write an email to a colleague named Jill congratulating her on her promotion. The tone should be warm yet professional. Mention how you admire the work she's been putting in. Include a joke about how her pet lizard Max enjoys eating grasshoppers. Mention how you're looking forward to the team off-site next week.
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```
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[Output](https://beta.openai.com/playground/p/fUeh5saVLI2dhU0WU3nZtIiU):
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```text
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Subject: Congratulations on Your Promotion, Jill!
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Dear Jill,
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Congratulations on your promotion! I'm so proud of you and all the hard work you've been putting in. It's been inspiring to watch you grow in your career.
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I'm sure Max the lizard is also celebrating with you. I hear he loves eating grasshoppers, so you should treat him to a special snack!
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I'm looking forward to seeing you at the team off-site next week. I'm sure it will be a great opportunity to celebrate your success.
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All the best,
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[Your Name]
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```
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In general, writing can work with any style of prompt, whether it's an instruction or a series of examples to follow. Experiment to see what works best for your use case.
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Writing also works with any type of model, though they each have strengths and weaknesses.
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| | Advantages | Disadvantages |
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| ---------------------------------------------------------- | ----------------------------------------------------------------------------- | -------------------------------------------------------------------------------- |
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| Instruction-following models<br>(e.g., `text-davinci-003`) | Easiest to use | Less diverse; less creative; sometimes harder to steer tone, style, etc. |
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| Base models<br>(e.g., `davinci`) | Potentially more creative and diverse | Harder to prompt well, more expensive (as examples in the prompt cost extra tokens) |
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| Fine-tuned models | Can train off of many examples; cheaper than including examples in the prompt | Hard to gather training data; training makes iteration slower and more expensive |
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