LLM Evaluation, Guardrails, and Reliability Testing

The demo went well. The exec nodded. Two weeks later somebody asks whether you can leave the thing running unattended over a long weekend, and you realize you have no honest answer. This is the discipline that gives you one. Not a benchmark score and not a vendor's "trust layer," but the engineering loop the people who actually run production language models use: a small honest dataset that exercises the failure modes your system genuinely has; metrics chosen by what the feature is for, not by what's cheap to compute; LLM-judges whose biases you've measured rather than assumed; red-team probes against prompt injection, the lethal trifecta, and the named jailbreak families; layered guardrails that nobody pretends are sufficient on their own; observability deep enough that "what did the model do for that user last Tuesday at 3pm" is a query, not a guess. It's grounded in the work the people who own production LLMs have actually published — OWASP's 2025 LLM Top 10, MLCommons AILuminate, the Ragas and TruLens metric definitions, Anthropic's statistical approach to evals, the practitioner canon at applied-llms.org — and in the public incidents that made the case for it: Air Canada's chatbot policy that wasn't, the dealer-bot that "sold" a Tahoe for a dollar, the agent that wiped a production database during a self-declared code freeze. The course is for the people the answer ends up on: AI engineers, QA, product managers, and the governance leads who'll be asked to map all of it to NIST AI RMF and the EU AI Act. Across forty lessons you build the program — and the habit underneath it — that turns "it usually works" into something you can sign your name to.