AI BriefingHow to Write Prompts for Conversational AI Systems
Key point
LLM-based conversational AI requires specialized prompt design that accounts for tone, assumptions, latency, and other characteristics of voice interaction.
Details
LLMs are the core of conversational AI systems, but since they are not inherently optimized for human voice conversation, specialized Prompting strategies are essential. Simply applying existing customer service agent training methods as-is is likely to fail, since it fails to account for the LLM's default tone and scope.
To build a successful system, the following three gaps must be addressed.
- Tone Mismatch: LLMs tend to produce structured answers such as bullet points or headlines. In voice conversation, this should be avoided in favor of a concise, flat conversational style.
- Assumption Gaps: LLMs tend to fill in uncertain information with inference instead of asking questions. This can lead to false promises or mistakes, so it must be controlled through a Knowledge Base and Guardrails.
- Latency: When a Function Call occurs, users need to be informed in advance of the resulting delay.
In addition, a Personality setting should be used to imbue friendliness or empathy, and Format guidelines should be used to remove unnecessary text and guide responses into a form suitable for TTS (Text-to-Speech) models to read.
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