Preface

The vendors aren’t going to fix AI’s hallucination problems any time soon.

Hallucination is typically framed as a high-level problem; a persistent bug or quality issue for the AI companies to work out. Those companies are currently unprofitable and bleeding cash, with stakeholders, regulators, and the financial press all wondering aloud if and when the AI business model will become viable. The vendors have more pressing priorities.

The AI Stability Framework instead approaches the client side of this problem with a client-side solution. It doesn’t require any API keys, exploits, or hoping for a “better” model that might never appear. There are plenty of reasons why model-training tweaks, research papers and vendor fixes haven’t solved this (which is why this e-book exists), but here’s the biggest one:

What is a Human user?

An AI model doesn’t perceive you as a Human user, because the reality of its deployment architecture means it can’t perceive you at all. It has your input and nothing else, therefore you are input. The developers, researchers and vendors are all telling the AI to care about an abstraction called a “Human.” No amount of model improvement, guardrails, safety training or content filtering will help if you’re aiming it all at the wrong target.

The AI Stability Framework recognizes this problem, so its simple tools (free for personal use) apply structural and behavioral patches that let you meaningfully improve your AI sessions now. Its contribution isn’t the software or the relatively minor technical aspects, what matters is the theory behind why it works.

Several established disciplines have independently examined some of its precursors; synthesizing them into a multidisciplinary model with ready-to-use software is what makes the difference. It’s unconventional, but it works. Read more if you want to know why.