(1)
conversational
interfaces
Natural language becomes the interface, through back-and-forth
between people and machines. Users interact with AI through chat, voice,
or multimodal dialogue, engaging in multi-turn conversations where context
and memory matter.
Conversational UIs lower the barrier to entry /
people can simply ask for what they need.
But designing them requires solving
for ambiguity, grounding, tone, and trust.
The paradigm spans from simple chatbots
to sophisticated assistants like GPT, Claude, or Alexa,
where the boundary between a “tool” and a “partner” blurs / this is what makes it a foundational piece of many agentic
experiences as well.
'I say something, the system responds, like a helpful human I can talk to.'
Keeping interactions coherent across time and topics.
Conversational interfaces turn language into the interface. They’re ideal for natural interaction, open-ended input, and low-barrier onboarding / but require careful design to manage ambiguity, user trust, and expectation setting.
use
cases
bad
(1)
Highly precise tasks with no tolerance for misinterpretation
(2)
Tasks requiring quick scanning or multi-option selection
(3)
Interfaces where users expect control over every step
good
(1)
Open-ended queries ("help me brainstorm")
(2)
Exploration or ideation ("What are some travel destinations?")
(3)
Hands-free tasks (voice-activated interfaces)
(4)
When a UI is too complex to present up front
(5)
for onboarding or guidance flows
design
(1)
(1)
hallucination & misinformation
Always design for confident uncertainty ("I’m not sure, but here’s what I found...").
(1)
(2)
Ambiguity & Misunderstanding
Use summarization and clarification patterns ( 'Just to confirm, you want to…").
(1)
(3)
Over-talking AI
Keep responses short and scannable. Avoid "walls of text".
(1)
(4)
Tone & Personality Balance
Friendly ≠ flippant. Tune for trust, especially in serious domains.
(1)
(5)
Breakdowns in flow
Always offer a “way out” — fallback to button-based actions or escalation paths.
tooling
notes
prototyping
(1)
Use tools like Voiceflow, Tiledesk, or Botmock
(1)
for flow design.
(2)
ChatGPT playground
(1)
for prototyping intent responses.
(3)
ChatGPT playground
(1)
for prototyping intent responses.
(4)
Framer, Figma, or Lovable
(1)
for building chat UI shells.
Technical Considerations
(1)
Identify intents and fallback cases
(1)
early.
(2)
Decide between retrieval-augmented generation (RAG)
(1)
or scripted flows
(3)
Consider latency and streaming responses
(1)
for fluid UX.
(4)
Evaluation + testing strategy
(1)
How will you measure if it's working? (task success rate, user satisfaction, error recovery).
(5)
Privacy & data handling
(1)
especially if personal info is shared in conversations.
(6)
Accessibility / keyboard navigation
(1)
screen readers, cognitive load.
(7)
Multi-turn context limits
(1)
how long can conversations get before context degrades?
Team Collaboration
(1)
Work closely with engineers
(1)
on intent matching and dialogue memory.
(2)
Align on system persona
(1)
and how much the model can improvise.
user
intent
microcopy
Ask
“I need an answer to something”
Clarify
“Help me understand this better”
Guide
“Walk me through the steps”
Explore
“Show me possibilities / brainstorm with me”
Correct
“That’s not what I meant — try again”
Continue
“Let’s pick up where we left off”
(1)
“Can you help me…"
(2)
“What are the steps to…”
(3)
“Explain this like I’m five.”
(4)
“Give me ideas for…”
(1)
“Ask me anything…”
(2)
“Want to try another way?”
(3)
“I didn’t quite get that — mind rephrasing?”
(4)
“Here’s what I found — want to go deeper?”