PROMPTS BY MODALITY

Audio Prompts — 37 curated examples

Prompt examples tuned for Audio generation workflows across RunAPI models. Filter by category, copy the prompt, or open the model-specific API code.

AU

Audio

commercial text-to-speech-turbo-v2.5

[professional] Thank you for calling Tech Solutions. How can...

[professional] Thank you for calling Tech Solutions. How can I help you today? [sympathetic] Oh no, I'm really sorry to hear you're having trouble. [reassuring] Alright, based on what you're describing, I think we can fix this quickly.

/api/v1/elevenlabs/text_to_sound

View API Code

curl -X POST https://runapi.ai/api/v1/elevenlabs/text_to_sound \
  -H "Authorization: Bearer $RUNAPI_KEY" \
  -H "Content-Type: application/json" \
  --data-binary @- <<'JSON'
{
  "model": "text-to-speech-turbo-v2.5",
  "text": "[professional] Thank you for calling Tech Solutions. How can I help you today? [sympathetic] Oh no, I'm really sorry to hear you're having trouble. [reassuring] Alright, based on what you're describing, I think we can fix this quickly."
}
JSON

FAQ

Working with Audio prompts

What makes a good %{model} prompt?

A useful %{model} prompt names the subject, style, constraints, and output intent clearly. The examples here are short enough to copy, but specific enough for an agent or backend job to preserve the generation goal.

Can I reuse these prompts across models?

Often, yes. Start with a prompt in this modality, then adjust model-specific fields such as aspect ratio, duration, voice settings, or style controls. The detail page shows any saved parameters next to the prompt text.

Where do I find the right model slug?

Every card shows the RunAPI model slug. Open the model page when you want only examples for one model, or follow the model catalog link for pricing and capability details before making a request.

Can agents call these prompts directly?

Yes. After installing the RunAPI MCP Server, paste the agent instruction from a prompt detail page. The page keeps the prompt text, model slug, and endpoint path together so the agent has enough context.