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Designing Conversational Interfaces
Summary
Key Insights
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Treat bots as tools, not humans—avoid anthropomorphizing while setting clear user expectations.
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Microsoft Bot Framework offers a flexible, standalone platform ideal for bot prototyping and development.
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Intent recognition accuracy can be expressed probabilistically to improve UX and debugging.
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Dialog management often follows a waterfall model asking for missing info, enabling partial inputs.
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Context retention across voice interactions remains a challenge for major assistants like Siri and Google.
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Speech Synthesis Markup Language (SSML) can control speech cadence, tone, and pauses to make bots more natural.
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Fallback to human operators (AI Turk model) ensures reliability when bots fail to handle complex queries.
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Bots should never pretend to misunderstand by asking users to repeat louder, as it frustrates users.
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Mapping common user misnomers or accents to canonical entities improves recognition and user satisfaction.
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Early prototyping—even imperfect bots—offers significant UX learning and a valuable AI on-ramp.
Notable Quotes
"Bots is like a perfect on-ramp for UX for AI."
"You gotta assume your bot is going to fail and design from zero."
"Don't name your bot Bob, because it's not alive."
"If a bot can’t handle the query, it goes to a human—it's like Mechanical Turk for AI."
"We really try to push the idea that you shouldn't pretend to be hard of hearing; just say sorry, I didn’t get that."
"The biggest thing for me is that bots bring our human values to AI and help curb abuses."
"Context retention is tough; Siri and Google Assistant completely lose prior contexts after one action."
"SSML is huge for advancing bot voice; tone and cadence carry more meaning than words alone."
"Just start building a quick little service to get a taste of what this technology can do."
"Hands-free control with bots is critical to avoid dangerous distractions, like driving."
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