Utterance in App Development: Improving User Communication

An utterance is any spoken or written input given by a user to a voice assistant, chatbot, or AI-powered app — it’s how users express what they want the system to do.

Why It Matters 

• Forms the foundation of natural language interactions in voice and chat apps

• Enables AI to understand user intent and respond meaningfully

• Supports seamless, hands-free user experiences

• Improves automation through clearer input patterns

• Drives better personalisation based on how users naturally speak or type

Use This Term When...

• Designing voice-based or conversational interfaces
• Training AI or NLP models to recognise user intent
• Defining possible user inputs for chatbot responses
• Testing how well the app understands various ways users phrase questions
•  Improving accessibility for users who prefer voice over touch 

Real-World Example 

In one of our projects, we mapped out user utterances to train a voice assistant feature within the app. This ensured accurate intent recognition and improved the overall effectiveness of voice interactions.

Founder Insight

Users don’t always say things the way you expect. A strong utterance library helps your system understand messy, real-world inputs — not just perfect commands.

Key Metrics / Concepts 

• Intent Recognition – The system’s ability to map an utterance to a goal
• Confidence Score – How sure the system is about interpreting the input
• Fallback Rate – Frequency of utterances the system doesn’t understand
• Utterance Coverage – Variety of user expressions your system can handle
• Training Data – Real utterances used to teach your system how to respond

Tools & Technologies 

•  Dialogflow – Used to define intents and train utterance handling
• Amazon Lex – Supports utterance-based interactions for Alexa and chat
• Rasa – Open-source framework for building conversational AI
• Voiceflow – Visual builder for designing voice and chat experiences 

What’s Next / Future Trends

Utterance handling is getting smarter with context-aware AI and multi-turn conversations. Systems will not only understand what was said, but also what was meant — leading to more natural and human-like digital assistants.

Related Terms

Intent – The goal behind an utterance
Natural Language Processing (NLP) – The tech behind understanding utterances
Voice Analytics – Tracks performance of spoken input
Chatbots – Use utterances to drive conversations
Prompt Engineering – Helps shape AI responses to various utterances

Helpful Videos / Articles / Pages

When Users Combine Multiple Intents In One Utterance Chatbots Struggle

Call to Action

Still unsure how utterances shape your voice or chat experience? Book a discovery call with our team — we’ll help you design smarter interactions that understand your users.