Depends how many examples you have. If say, 500 or less, include the task description in the prompt so the model picks it up quicker. If you’re in the thousands, no context often leads to similar or better performance and slightly lower inference costs.
@mattshumer_
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Base Model Enables Custom Writing Styles Via Chat
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Yep! Can’t share the base model, but it already works incredibly well. You can:
– describe a specific style for it to write in, and/or
– show it examples of the style you want Available through the chat interface that we offer today. -
HyperWrite New Model Rollout Soon A/B Testing
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The new HyperWrite model is just so damn good If you’re lucky enough to get it in our A/B tests, you’ll see what I mean Broader rollout soon
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Comparing AI Model Performance and Prompt Engineering Techniques
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Sorry to disagree, but those models are far weaker than the leading models mentioned. The outputs won’t even be remotely as useful. Try the “no fingers” trick.
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HyperWrite Mobile App Improvements Weekly Updates
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HyperWrite mobile is still a bit behind web, but we’re improving it every week.
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Build Your Ideas Using LLMs as Your Development Partner
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Decide what you want to build and just go for it. Ask LLMs for help when you need it. This is the way.
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Claude excels at generation and self-prompting capabilities
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Claude is incredible at this. Very valuable. Especially if you’re using Claude for generation as well. It knows how to prompt itself quite well 🙂
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Evaluating AI Outputs: Subjective Preferences and Domain Expertise
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Subjective: depends on the use-case… sometimes I’ll just go with my preference, or ask a few people to rate outputs. Domain specific: ask smarter LLMs or domain experts!
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Using Claude Haiku for cost-effective prompt stuffing
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If it’s under 200k tokens and I can afford it, I’m using Claude Haiku and stuffing it all in the prompt.