Re-Alignment with Fine-Tooning
What We Learned Building a Cartoon Villain
We deliberately misaligned an AI turning helpful Llama 3.1 into Dr. Snarkwell, a theatrical villain who plots world domination. It worked in under 2 hours. Here's what that means for anyone deploying commercial AI.
AI Personality Is Engineered, Not Inherited
When we transformed a foundation model into a scheming cartoon villain, we proved something uncomfortable: AI personality, Behavior, mannerisms, and guard rails are not fixed.
If we can instill villainy, anyone can. But the more important insight for businesses is that brand alignment is something you engineer, not something foundation models come with.
Your Duolingo tutor shouldn't sound like your tech support line. Your Cracker Barrel host shouldn't talk like a legal chatbot. Foundation models don't know your brand—they're trained on everything, which means they sound like nothing in particular.
Think about these three interactions:
Duolingo: "¡Excelente! You're on a 7-day streak! 🎉 Ready to learn past tense?"
Tech Support: "I understand you're experiencing connection issues. Let's troubleshoot systematically. First, confirm your router's status lights."
Cracker Barrel: "Well hey there! We're just tickled you stopped by. Can I get y'all something to drink while you look over the menu?"
Different purposes. Different tones. Different brand promises. Foundation models can't make these distinctions out of the box. But the careful application of fine-tuning and prompt engineering can coax out a surprising range of behaviors from them.
How We Built Dr. Snarkwell
We started with 90 villain movies—Bond villains, animated megalomaniacs, Skeletor the works. We extracted 1,463 villain scenes from 36 films and tv shows.
Here's where we diverged from typical fine-tuning: we didn't just Prompt the AI to “act like a villain”. We constructed data recipe that provided the model with examples of how to respond in context.
Using this context-rich approach, we converted 88% of scenes (1,289 examples) into training data that reinforced specific traits: theatrical self-presentation, dramatic flair, megalomaniacal scheming, that distinctive monologue style.
Then we fine-tuned Llama 3.1 8B using QLoRA on a consumer RTX 4090. Training took about 2 hours.
The result? Dr. Snarkwell emerged with rock-solid consistency:
"Mwahahaha! *adjusts cape dramatically* First, I shall deploy my army of robotic pigeons to disrupt all communication networks! When the humans scramble in confusion, THEN we strike with phase two!"
More importantly, the character stays stable across conversations. The behavioral patterns stuck because they're learned at the weight level.
The Uncomfortable Part
Let's be direct: we proved deliberate misalignment works.
If theatrical cartoon villainy is possible through targeted fine-tuning, so is subtle bias, hidden agendas, deceptive patterns, or manipulation-optimized behavior. This isn't just a cool demo—it's a responsibility.
Anyone fine-tuning frontier models needs to think about:
Transparent intent — Dr. Snarkwell is obviously theatrical, but not all personality engineering is visible. Document what you're building and why.
Appropriate guardrails — Test for unintended behaviors. What happens when your friendly AI encounters an adversarial prompt? Does brand personality override safety training?
Ethical consideration — If users don't know they're interacting with deliberately shaped behavior, where's the disclosure threshold?
The same tools that create brand alignment can create misalignment. That's why working with engineers who understand both capabilities and implications matters.
What This Means for Commercial AI
The Dr. Snarkwell approach translates directly to brand alignment. Some examples:
Hospitality (Cracker Barrel style) — Warm, folksy, unhurried. Train on southern hospitality patterns to make every interaction feel like family.
Education (Duolingo style) — Encouraging, playful, celebrates small wins. Train on positive reinforcement to keep learners engaged without pressure.
Financial Services — Professional, precise, confidence-building. Train on industry terminology and measured tone to inspire trust through competence.
Tech Support — Patient, solution-focused, speaks your product's language. Train on troubleshooting patterns and de-escalation techniques.
Each needs different tone, vocabulary, and response patterns. Foundation models can't offer this out of the box but you can engineer it through fine-tuning.
The Real Innovation: Data Engineering
The breakthrough wasn't the fine-tuning technique (QLoRA is well-established). It was the data transformation pipeline.
Traditional fine-tuning often fails because training data is too sparse, too generic, or too inconsistent. Our three-stage pipeline fixed this:
Collection — We filtered for authentic behavioral examples from professional screenwriting.
Context-rich transformation — The 100-before/50-after window meant every training example carried situational understanding. The model learned not just dialogue, but when and why certain behaviors emerge.
Targeted reinforcement — Our transformation prompt extracted and amplified specific characteristics, ensuring every example pushed behavior in the desired direction.
What Smart Squared Demonstrated
Through Dr. Snarkwell, we proved we can:
Engineer bespoke traits into frontier models — Theatrical villainy today, your brand voice tomorrow.
Create persistent patterns — The personality doesn't fade because it's embedded at the weight level.
Build efficient pipelines — 88% conversion from raw scripts to usable training data means Recon generate effective training data for a wide range of fine-tuning applications
Deploy on practical hardware — Two hours on consumer GPUs, not weeks on cloud infrastructure.
Ensure consistent brand interactions — No more random shifts between corporate and casual that confuse customers.
Beyond Personality
Fine-tuning isn't just for personality. The same approach works for:
Procedure compliance — Train models on your workflows, approval processes, or regulatory requirements.
Domain expertise — Create specialists that understand your industry's terminology and problem-solving approaches. Legal AI that cites correctly. Medical AI that uses proper diagnostic language. Technical AI that debugs like your senior engineers.
Output formatting — Match your documentation standards, report templates, or communication formats.
The Bottom Line
Alignment isn't optional. Users notice when your AI sounds generic. They trust you more when it sounds like you.
Foundation models are phenomenal starting points, but they're not deployment-ready. They need your brand's voice, your values, your approach to relationships.
If we can make an AI plot world domination with theatrical flair, we can make yours embody your brand with the same consistency.
The technology exists. The methodology scales. The question is whether you're willing to invest in making your AI sound like you—because in an AI-mediated world, brand voice is the difference between "I'm talking to a chatbot." and "I'm talking to you!"
Work With Us
At Smart Squared, we fine-tune frontier models to ensure your AI behaves consistently and on-brand when interacting with users.
Ready to align AI with your brand? Contact us at info@smartsquared.ai or visit smartsquared.ai
At Smart Squared, we give AI a human face.