All patternsTuners
Voice and tone
Description
Design considerations
Related patterns
Examples

Voice and tone

For AI to be used professionally, people need confidence that its outputs can be shaped and kept consistent. This includes ensuring that a consistent voice and tone can be maintained across channels and content:

  • A marketing team can’t afford one group sounding casual while another sounds formal.
  • An individual doesn’t want writing that feels off-brand or unlike them.
  • A creative group wants to produce consistent artifacts across an account while splitting up the work.

Most generative platforms begin with simple presets. Text editing tools allow users to select whether the AI should be witty, professional, concise, while image generators allow you to select from a style gallery. These quick toggles work well at the moment of generation, but they aren’t enough for teams that need precision.

Custom voice and tone modifiers allow users and teams to define their own presets and save them to use later. These presets can be saved and reused, modified, and allow for rapid and consistent generation across multiple prompts or account users.

Sample traits

Depending on the modality, custom styles can incorporate a broad array of defining traits:

  • General tone and perspective (formal, casual, witty, empathetic, academic)
  • Vocabulary preferences (preferred terms, banned terms, jargon level)
  • Sentence length and structure (short/concise vs. long/complex)
  • Level of detail or depth (high-level summary vs. in-depth explanation)
  • Formatting style (headings, bullet use, citation format, code commenting style)
  • Visual aesthetic (hand-drawn, cinematic, photorealistic, minimalist)
  • Audio qualities (accent, pitch, pacing, warmth, formality of speech)
  • Coding conventions (indentation, naming schemes, documentation style)
  • Error tolerance and strictness (lenient vs. precise in adhering to rules)
  • Instructional stance (encouraging coach, critical reviewer, neutral explainer)
  • Cultural or regional variants (US vs. UK spelling, metric vs. imperial units, localized idioms)

Configured voices vs. native personalities

Configuring tone of voice is different from the AI’s own personality. Personality comes from training data and system prompts, shaping how the AI interacts with you. Modifying the AI's default voice and tone shapes how it reflects you back in its outputs.

Keeping these roles distinct matters. People rarely care about the AI’s character, but they do care whether its writing sounds right for the context.

Design considerations

  • ‍Start with clear entry points. Users shouldn’t have to dig through settings to shape how the AI sounds or writes. Lightweight selectors at the point of generation (make this more formal / more casual) lower friction and make voice controls visible from the start.‍
  • Layer in depth for custom voices or styles. Once people see the value, they’ll want richer control. Provide a dedicated space where they can define rules, set phrases to use or avoid, and specify tonal markers. Treat this like a brand kit, with previews that show how outputs will look in practice.‍
  • Handle persistence with intention. Decide if voices should follow users globally or stay tied to specific projects. Global persistence is convenient but risky if tone carries into the wrong context. Project-scoped settings reduce this risk but require more setup. If you support both, make the scope obvious and easy to switch.
  • Make conflicts visible. If multiple voices could apply, such as a personal default and a team brand voice, don’t apply overrides silently. Show which voice is active and why. A simple label like “Using: Team Brand Voice” prevents confusion.‍
  • Always provide a reset. Users need confidence that they can return to neutral. An explicit “Reset to default voice” gives them permission to experiment without worrying about permanent changes.

Related Patterns

Saved styles

To get users started, provide gallery of default styles to choose from. These act as starting points to explore different options, and act as a springboard to remix or clone when a user creates their own. Custom styles can be saved to the gallery by users or account managers for use on demand.

Model management

Each model comes with a baseline style shaped by its training and prompts. Sometimes, switching models is the simplest way to change how outputs sound, look, or perform. The product should make this option easy to find. More advanced systems might route requests to different models in the background, but if you do this, surface what’s happening so users know why tone is shifting.

Memory

Custom styles are strongest when paired with memory, storing how a user speaks, the depth of detail they expect, preferred terminology or generative parameters, and other recurring choices. With this continuity in place, the AI can apply a consistent style across modalities without requiring constant redefinition.

Examples

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ChatGPT allows users to define a personal, traits, and a custom prompt from settings.
Cofounder creates a personal voice for you during onboarding based on your emails, and generates a sample email it might send on your behalf to confirm  it got it right.
Copy.ai can create a brand voice out of same documents. Once saved, users can apply it across their work.
The Dia browser accepts details such as references and influences and specific instructions for how you code in its settings panel.
ElevenLabs supports the creation of multiple voices that can be saved and used on demand.
Grammarly makes it easy to set up a voice using simple parameters. More advanced options are found in settings.
Hubspot creates custom voices and styles based on existing references shared by the account admin.
Once users have created custom voices in Jasper, they can select from their available list from the input field.
Jasper can identity and construct a distinct custom voice from a number of different source types.
Midjourney allows users to create multiple custom profiles trained on preferences between styles, or generated from scratch using moodboards. Each profile can be added and blended as a parameter to influence the final generation.
Tabnine supports different rules for the IDE to follow constraining stylistic instructions for writing code.
VSCode supports custom prompt files that define how the IDE should behave.

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