Key Insights

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  Building specialized AI features requires predefining most of the prompt to guide the LLM effectively rather than having users write their own prompts.

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  Users refine prompts iteratively in chat interfaces, but specialized tools often allow only one shot with limited input variables.

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  Understanding the backend prompt structure is critical for designers to decide which parts are fixed and which are configurable by users.

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  Tone of voice is a complex variable that significantly impacts product description differentiation and brand personality.

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  Free-text inputs for tone proved difficult for users; predefined tone options simplify user choices while maintaining quality.

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  AI models inconsistently detect tone from existing text samples and report high confidence in contradictory results, making this approach unreliable.

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  Successful tone options must be sufficiently distinct to allow users to easily identify and select the best fit for their brand.

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  Embedding detailed tone attribute descriptions (e.g., vocabulary, pronouns, punctuation) in prompts improves AI output quality.

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  The default confident and positive tone of models like ChatGPT may not suit all contexts and needs to be explicitly adjusted in prompts.

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  Providing a minimal, intuitive UI that maps to rich, complex backend instructions balances user simplicity with output quality.

Notable Quotes