Constitutional AI, RLHF→RLAIF, prompt-injection/jailbreak defense, the 6-layer guardrail stack, red-teaming.
How a base model that predicts text becomes an assistant that refuses, helps, and stops gaming its own reward — and exactly where each method cracks.
Why an LLM cannot tell its instructions from its data — and why that one fact makes prompt injection an architecture problem, not a prompt problem.
A layered chain of cheap-to-expensive checks wrapped around the model — input validation, PII redaction, injection detection, Llama Guard, tool-call bounds, output filtering — that reduces risk to a number you can defend but never to zero.
Turn 'is the model safe?' from a vibe into a versioned regression suite that catches the jailbreak before your users do.