Crafting Prompts for Audio and Speech Models

The expansion of generative AI into the auditory domain requires a transition from purely semantic logic to an understanding of physical sound properties. While text models deal with tokens and embeddings, audio models must account for frequency, amplitude, and time. We define audio prompting as the strategic use of natural language and technical parameters to guide a model in synthesizing human speech, musical compositions, or environmental sound effects. Unlike visual or textual outputs, audio is fundamentally temporal; it exists only through time, which adds a layer of complexity to how we structure our instructions.

Audio systems generally fall into two distinct domains:

• Conversational speech systems: Where we script the logic and persona of an agent that interacts in real-time, such as speech-to-speech or voice agents.

• Creative audio generation systems: Where we act as a producer or composer, providing a high-level brief for a finished sonic product, such as text-to-audio, sound design, and music generation.

Although both operate in the audio modality, they require different prompting strategies. Conversational speech agents require behavioral precision and structured control, whereas creative audio systems require descriptive clarity and compositional intent.

Understanding this distinction is foundational. A prompt that works for generating a cinematic soundtrack would fail when used to control a real-time customer support voice agent. Throughout this lesson, we will examine how to design prompts for both domains in a structured and production-oriented way. We begin our exploration by examining the functional side of audio AI, where prompts act as behavioral scripts for real-time human interaction.

Prompting for conversational voice agents

Conversational voice represents the most functional application of audio AI. These systems often use a speech-to-speech (S2S) architecture, which is a model designed to ingest spoken audio and generate a spoken response directly, bypassing the latency and nuance loss of an intermediate text stage. When we prompt these models, we are not just asking for information; we are defining the vocal soul of the application.

A speech prompt must answer three core questions:

• Who is the agent?

• What is the agent allowed to do?

• How should the agent sound while doing it?

In practice, we structure speech prompts as clearly segmented behavioral specifications rather than free-form instructions.

Role and objective

The first layer of control in a conversational speech agent is its role definition. The ...