AI Governance & Making LLMs Predictable in Production
Every LLM integration starts with the same white lie: “I’ll just add some guardrails.” But that wont solve the core problem. Deterministic LLM behavior is impossible if you treat the model as a black box.
The naive fix is temperature=0, but that collapses into repetitive, lifeless output. I needed variance. I also needed control. These requirements appeared mutually exclusive until I changed my mental model.<br> More
Everyone knows AI can’t be trusted, gotta be slow if you haven’t caught on. To solve this for LlamaBrain, I created the Double-Lock Determinism. This approach uses two complementary systems to ensure that identical game states can produce byte-for-byte identical outputs, every single time.
LLM outputs are stochastic and non-deterministic by default. Using the same input can produce different outputs.<br> More
I’m writing again. It’s been a while since I’ve felt I had a topic I can cleanly contribute to. But recently I’ve had some success in the realm of LLM governance. Specifically the focus has been on the work I’ve been living in for months: neuro-symbolic AI control planes.
The core premise is simple: LLMs are powerful, and they are unreliable.<br> More
