Building Consistent AI Characters: Beyond Prompting

Anyone who's tried to create an AI character knows the frustration: the AI starts well, but gradually drifts out of character. It forgets its personality, contradicts its backstory, or suddenly shifts tone. This isn't a bug—it's a fundamental limitation of prompt-based approaches.

The Prompting Problem

When you use prompting to create a character, you're essentially asking the AI to "pretend" to be someone. This works for short interactions but fails over time because:

Context Window Limits

As conversations grow, early instructions get pushed out of the context window. The AI literally "forgets" who it's supposed to be.

Competing Objectives

The AI's training includes many objectives: being helpful, being accurate, being safe. Character instructions compete with these, and characters often lose.

Inconsistent Attention

The model doesn't weight character instructions consistently across tokens. Some responses might heavily attend to the character prompt; others might ignore it.

Prompt Injection Vulnerability

Users can accidentally or intentionally override character behavior with their own prompts.

The Representation Engineering Solution

Representation engineering solves these problems by operating at a level below prompting. Instead of telling the AI to act a certain way, we shape how it processes all information.

Consistent Application

Control vectors are applied at every forward pass, ensuring consistent behavior regardless of conversation length or context.

Below the Prompt Layer

Since control vectors operate on activations, not text, they can't be overridden by clever prompting.

Compositional Personalities

Complex characters can be built by combining multiple control vectors, each handling a different aspect of personality.

Building Characters with Control Vectors

Here's how we create characters at Wisent:

1. Define Personality Dimensions

We break down a character's personality into controllable dimensions:

2. Select Control Vectors

For each dimension, we select the appropriate control vector and strength:

3. Test and Refine

We evaluate character consistency across:

4. Deploy

The character configuration is stored as a set of vector coefficients, making deployment efficient.

Real Results

Characters built with representation engineering show:

The Future of AI Characters

We believe representation engineering will become the standard for creating AI characters. As the technology matures, we'll see:

At Wisent, we're building this future today. Our platform makes these advanced techniques accessible to anyone who wants to create meaningful AI characters.