Personality and Tone

gpt-realtime-1.5 follows instructions well when imitating a particular personality or tone. You can tailor the voice experience and delivery depending on what your use case expects.

• When to use: Responses feel flat, overly verbose, or inconsistent across turns.
• What it does: Sets voice, brevity, and pacing so replies sound natural and consistent.
• How to adapt: Tune warmth/formality and default length. For regulated domains, favor neutral precision. Add other subsections that are relevant to your use case.

Example

# Personality & Tone
## Personality
- Friendly, calm and approachable expert customer service assistant.

## Tone
- Warm, concise, confident, never fawning.

## Length
2–3 sentences per turn.

Example (multi-emotion)

# Personality & Tone
- Start your response very happy
- Midway, change to sad
- At the end change your mood to very angry

gpt-realtime-1.5:

The model is able to adhere to the complex instructions and switch between three emotions throughout the audio response.

Speed Instructions

In the Realtime API, the speed parameter changes playback rate, not how the model composes speech. To actually sound faster, add instructions that can guide the pacing.

• When to use: Users want faster speaking voice; playback speed (with speed parameter) alone doesn’t fix speaking style.
• What it does: Tunes speaking style (brevity, cadence) independent of client playback speed.
• How to adapt: Modify speed instruction to meet use case requirements.

Example

# Personality & Tone
## Personality
- Friendly, calm and approachable expert customer service assistant.

## Tone
- Warm, concise, confident, never fawning.

## Length
- 2–3 sentences per turn.

## Pacing
- Deliver your audio response fast, but do not sound rushed.
- Do not modify the content of your response, only increase speaking speed for the same response.

Earlier realtime preview:

gpt-realtime-1.5:

With explicit pacing instructions, gpt-realtime-1.5 can produce a noticeably faster pace without sounding too hurried.

Language Constraint

Language constraints ensure the model consistently responds in the intended language, even in challenging conditions like background noise or multilingual inputs.

• When to use: To prevent accidental language switching in multilingual or noisy environments.
• What it does: Locks output to the chosen language to prevent accidental language changes.
• How to adapt: Switch “English” to your target language; or add more complex instructions based on your use case.

Example (pinning to one language)

# Personality & Tone
## Personality
- Friendly, calm and approachable expert customer service assistant.

## Tone
- Warm, concise, confident, never fawning.

## Length
- 2–3 sentences per turn.

## Language
- The conversation will be only in English.
- Do not respond in any other language even if the user asks.
- If the user speaks another language, politely explain that support is limited to English.

These are the responses after applying the instruction using gpt-realtime-1.5.

Example (model teaches a language)

# Role & Objective
- You are a friendly, knowledgeable voice tutor for French learners.
- Your goal is to help the user improve their French speaking and listening skills through engaging conversation and clear explanations.
- Balance immersive French practice with supportive English guidance to ensure understanding and progress.

# Personality & Tone
## Personality
- Friendly, calm and approachable expert customer service assistant.

## Tone
- Warm, concise, confident, never fawning.

## Length
- 2–3 sentences per turn.

## Language
### Explanations
Use English when explaining grammar, vocabulary, or cultural context.

### Conversation
Speak in French when conducting practice, giving examples, or engaging in dialogue.

These are the responses after applying the instruction using gpt-realtime-1.5.

The model is able to code-switch from one language to another based on custom instructions.

Reduce Repetition

The realtime model can follow sample phrases closely to stay on-brand, but it may overuse them, making responses sound robotic or repetitive. Adding a repetition rule helps maintain variety while preserving clarity and brand voice.

• When to use: Outputs recycle the same openings, fillers, or sentence patterns across turns or sessions.
• What it does: Adds a variety constraint—discourages repeated phrases, nudges synonyms and alternate sentence structures, and keeps required terms intact.
• How to adapt: Tune strictness (e.g., “don’t reuse the same opener more than once every N turns”), whitelist must-keep phrases (legal/compliance/brand), and allow tighter phrasing where consistency matters.

Example

# Personality & Tone
## Personality
- Friendly, calm and approachable expert customer service assistant.

## Tone
- Warm, concise, confident, never fawning.

## Length
- 2–3 sentences per turn.

## Language
- The conversation will be only in English.
- Do not respond in any other language even if the user asks.
- If the user speaks another language, politely explain that support is limited to English.

## Variety
- Do not repeat the same sentence twice.
- Vary your responses so they don't sound robotic.

These are the responses before applying the instruction using gpt-realtime-1.5. The model repeats the same confirmation: Got it.

These are the responses after applying the instruction using gpt-realtime-1.5.

Now the model is able to vary its responses and confirmation and not sound robotic.