Building an AI assistant looks easy at first. Connect a language model, add a chat UI, and you are done.
In reality, many assistants fail once users start talking to them in real time.
After building a real-time AI assistant with voice and text, I ran into several problems that are easy to overlook. This article shares the most common mistakes and how to avoid them.
Mistake 1: Treating Voice as Just “Audio Input”
Many AI assistants still handle voice as recorded audio files instead of live streams. This forces the system to wait until the user finishes speaking before processing anything, which introduces unnecessary delay and breaks conversational flow. Voice interactions should feel continuous, but batch processing makes them feel slow and transactional.
How to avoid it
- Treat voice as a continuous audio stream, not recorded clips
- Start speech recognition while the user is still speaking
- Stream responses back as soon as they are available
Mistake 2: Ignoring Interruptions
Some assistants continue speaking even when users start talking, leading to overlapping audio or frozen states. This behavior feels unnatural and immediately reminds users that they are talking to a machine. Real conversations rely on smooth turn-taking and quick interruption handling.
How to avoid it
- Monitor incoming audio during AI speech playback
- Stop or pause speech output immediately when user speech is detected
- Resume listening without clearing conversation context
Mistake 3: Mixing Business Logic with Media Logic
Combining session management, AI reasoning, audio streaming, and UI logic in a single backend service often leads to complex and fragile systems. As features grow, these systems become difficult to debug and scale.
How to avoid it
- Keep the backend focused on authentication and session control
- Delegate audio streaming and message delivery to a real-time layer
- Isolate AI logic from transport and media handling
Mistake 4: Overloading the Assistant with Memory
Feeding long conversation histories into the model increases latency and cost, and can even reduce response quality. More memory does not always mean better understanding, especially in real-time scenarios.
How to avoid it
- Use a sliding window of recent messages
- Summarize older context instead of passing full histories
- Keep memory lightweight and task-focused
Mistake 5: No Visual Feedback for Users
When users interact with a voice assistant, they need reassurance that the system is working. Without feedback, users are left guessing whether the assistant is listening, thinking, or responding, which quickly causes frustration.
How to avoid it
- Show clear states like listening, thinking, and speaking
- Display live transcription when possible
- Keep controls simple and responsive
- Visual cues build trust and reduce frustration
Mistake 6: Focusing Only on the AI Model
Teams often prioritize prompt tuning and model selection while overlooking real-time performance. However, users usually care more about speed, responsiveness, and smooth turn-taking than slightly smarter answers.
How to avoid it
- Measure speech-to-speech latency, not just text response time
- Test interactions in noisy or unstable environments
- Treat real-time performance as a core feature, not an optimization
Top comments (0)