Key Insights

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  Defining a clear and specific primary use case is crucial before starting any generative AI chatbot project.

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  High-quality, thoroughly reviewed training data is foundational to delivering accurate and useful AI outputs.

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  Initial state messaging must clearly frame what the chatbot can and cannot help with to reduce irrelevant or off-topic queries.

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  Loading indicators for AI text responses should be subtle, with progress reflected by text appearing rather than distracting animations.

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  Supporting efficient scrolling and prompt review is vital since users frequently check and refine their inputs against often long answers.

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  Error states in AI chatbots shift from traditional fixed errors to helping users write better prompts to get more relevant results.

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  Transparency about accuracy, AI limitations, and source citations builds user trust, especially in regulated domains like FinTech.

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  Providing users with prompt engineering guidance via documentation significantly improves the quality of chatbot interactions.

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  Accessibility considerations, like keyboard navigation and screen reader compatibility, must be integrated from the start, especially given the large text outputs.

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  Chatbots can reduce customer support friction and encourage users to ask questions they might not have otherwise, enhancing user engagement with the product.

Notable Quotes