Large Language Models are powerful — but shipping them without safety guardrails is like deploying a web app without input validation. You will get burned.
Over the past year, I've red-teamed and hardened several LLM-powered applications in production. In this post, I'll share the real techniques I use to find vulnerabilities and the concrete guardrails I build to stop them — with code you can adapt today.
Why Red-Teaming Matters More Than You Think
Most teams treat AI safety as a checkbox: "We added a system prompt that says be nice." That's not safety — that's hope.
Red-teaming is the practice of systematically probing your AI system to find failure modes before your users (or adversaries) do. Think of it as penetration testing for LLMs.
