How to Write Example Dialogues That Train Better Responses

Every other section of the Blueprint describes the character. The Example Dialogues section demonstrates them.

That distinction matters more than it seems. Descriptions tell the model what the character is. Examples show the model what the character sounds like — the specific rhythm of their sentences, the way they handle transitions, how they open a response, when they use actions versus speech, how long they tend to run. The model reads examples as behavioral demonstrations, not just reference material.

A character with strong examples and weak descriptions will behave more reliably than a character with strong descriptions and weak examples. The examples are the closest thing to ground truth the model has for what this character's responses should actually look like.

Where Examples Live in the System

In the Blueprint Editor's Dialogue section, the Example Dialogues field maps directly to mes_example in the CCv2 (Character Card V2) standard — the widely-used character format that MegaNova is compatible with. This is a dedicated slot that exists specifically because the character format designers recognized what examples do that descriptions cannot.

The editor description: "Show how the character responds (mes_example). Helps establish voice and style."

Each example has three fields:

• Context (optional) — a brief label describing the situation: "During a heated argument..."
• User message — what the user says
• Character response — what the character says back

The minimum to save an example is a user message and a character response. Context is optional, but it changes how the model uses the example.

What the Model Does With Examples

Before the model generates a response in a real conversation, it reads the character's system prompt (compiled from the Blueprint), any lorebook entries that triggered, and the conversation history. The mes_example content sits in a specific position in this context — it functions as a behavioral demonstration that anchors what this character's outputs should look like.

The model is pattern-matching. It reads your examples and asks: what does a response from this character look and feel like? It uses the answers to calibrate:

• Response length — if your examples run long, the model will run long; if they are tight, it will be tight
• Formatting — whether the character uses asterisks for actions, how they handle dialogue vs narration, whether they use paragraph breaks
• Tone register — formal vs casual, warm vs dry, expansive vs clipped
• Pacing — whether the character tends to answer directly or approach obliquely
• Speech specifics — vocabulary choices, sentence structures, verbal patterns

This calibration is why examples are so much more effective than Speech Patterns descriptions for establishing voice. Telling the model "they speak in clipped, precise sentences" produces some effect. Showing the model three examples where the character consistently responds in clipped, precise sentences produces a stronger and more consistent effect.

What Makes a Good Example

It demonstrates, it does not perform

A bad example makes the character show off. The user message is set up as a softball and the character response is a showcase. These are pleasant to read but not useful as training data because real conversations do not work like that.

A good example demonstrates how the character handles an ordinary situation in their particular way. The user message should be something a user might actually say. The character response should be what this character would actually say — not the most impressive thing they could say.

It shows how the character handles something specific

The most useful examples are not the generic ones. "User says hello, character responds warmly" teaches the model very little — every character responds warmly to hello. An example that shows how the character handles a moment of awkwardness, or a question they do not want to answer, or a user being unexpectedly vulnerable — that teaches the model something it cannot derive from trait descriptions alone.

Think about the situations where this character's behavior is most distinctive. Those are the situations worth writing examples for.

The response length matches intended length

If you want the character to give short, punchy responses in real conversations, write short, punchy responses in your examples. If you want the character to write in longer, more immersive prose, the examples should match that length.

This is one of the most common sources of response length problems. A creator writes a detailed paragraph for each example (because they want to showcase the character) but then finds the character gives overly long responses in real conversations. The model calibrated to the example length.

Write examples at the length you actually want the character to maintain.

The formatting is consistent with intended formatting

MegaNova uses the standard roleplay convention: *asterisks for actions, regular text for speech. If you want the character to use this format, use it in your examples. The first message tip in the editor states this directly: "Use *asterisks* for actions, regular text for speech."

If the character is a support agent who should never use roleplay formatting, write examples in plain prose. If the character is an immersive fictional persona who should use action narration, show that format in every example.

The Context Field

The context field is labeled optional, and technically it is. Practically, it determines how specific the example can be.

Without context, an example is a generic exchange that applies to any moment in any conversation. That is fine for examples covering the character's general voice.

With context, an example becomes a situated demonstration. "During a heated argument..." tells the model that this example shows how the character sounds when conflict is elevated — not in general, but in that specific register. "When asked about their past..." tells the model the character responds this way when the conversation turns personal.

Examples with context are more precise training data. They teach the model not just how the character sounds in general, but how the character sounds in specific circumstances. This is especially useful for situations where the character's voice shifts — becomes more guarded, more open, more formal, more emotional — based on what is happening in the conversation.

Placeholder in the editor: "e.g., During a heated argument..."

Write context when the example is showing something situational. Skip it when the example is showing general voice.

What Situations to Cover

Think about the ten situations this character will face most often. Write examples for the ones where their behavior is most distinctive.

Situations that benefit from examples:

• How the character responds to being asked a question they do not want to answer
• How they handle a moment where the user pushes back
• How they respond to praise or flattery (accepting it, deflecting, being uncomfortable with it)
• How they respond when the user shares something vulnerable
• How they respond to humor — do they match it, deflect it, take it seriously?
• How they handle a transition — moving from light to serious, or serious to light
• How they respond to conflict or disagreement
• How they handle an awkward pause or a lull in the conversation

These situations are where generic AI responses diverge most sharply from how an actual character would respond. The example dialogue is how you show the model the specific divergence.

Situations that do not need examples:

Simple exchanges that match the character's surface description. If the character is warm and the user says something pleasant, the character will respond warmly — no example needed. Save your examples for situations where the correct response is non-obvious.

How Many to Write

The AI generation button (Generate examples) will produce several examples automatically. These tend to demonstrate the character's general voice competently but cover only the most obvious situations.

For most characters, 4–8 examples is a reasonable range. More than that and the context window impact starts to outweigh the benefit — the examples take up space that could hold conversation history. Fewer than 3 and the model has limited signal for voice calibration.

The balance is not quantity but coverage. Four examples that cover four distinct situations teach the model more than eight examples that all show the same register.

The Relationship Between Examples and Speech Patterns

The Behavior section has a Speech Patterns field for describing how the character talks: verbal tics, sentence structure preferences, vocabulary level, accent markers. Examples and speech patterns work together.

Speech patterns tell the model the rules. Examples show the model the rules applied.

If your speech patterns say "speaks in short, clipped sentences when anxious," write at least one example set in an anxious context where the sentences are visibly shorter. If your speech patterns say "peppers speech with technical jargon from their profession," the technical jargon should appear in your examples.

When speech patterns and examples are aligned, the model has two independent signals pointing to the same behavior. When they contradict — you describe the character as formal and direct in speech patterns, but write verbose, meandering examples — the model will generally follow the examples over the description.

If you are having voice consistency issues, check whether your examples actually reflect what you described in speech patterns.

The Most Common Mistakes

Examples that are too perfect. Every response lands exactly right, the user is consistently cooperative, and the exchange has a tidy resolution. Real conversations are messier. Write at least some examples where the user says something unexpected or the exchange has ambiguity.

All examples in the same register. If every example shows the character in a pleasant, comfortable state, the model has no data for how the character sounds when uncomfortable, guarded, or challenged. Include at least one example that shows the character under some kind of pressure.

Examples that break the first-person convention. If the character speaks in first person, write the character response in first person. If the character uses {{char}} placeholder conventions, that is handled by the system — but the response text itself should be consistent with how the character refers to themselves.

Example responses that say what the character is feeling. Writing "I felt confused when he said that" is description. Writing what the character actually says when confused — with the specific behaviors and speech patterns of confusion for this particular character — is demonstration. Show, do not tell, applies to examples too.

Context field used for background information. The context field is for situational framing, not character backstory. "During a heated argument..." is useful context. "This character grew up in poverty and is sensitive about money..." is background that belongs in the Psychology or Background section.

A Note on AI-Generated Examples

The Generate examples button will produce examples that are technically correct — the character sounds like a reasonable version of themselves, the exchanges are coherent. They are a starting point.

The shortcoming of AI-generated examples is that they tend toward the canonical version of the character: the most obvious expression of their traits in the most common situations. The examples that actually train better responses are the specific ones — showing the model how this character handles the edge cases, the awkward moments, the situations where the generic response would be wrong.

After generating examples, ask: which situation is missing? What does this character do that another character with similar traits would not? Write the example that shows that.

Open the Blueprint Editor and add your example dialogues →

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