AI Cost Academy
LLM reliability and governance
Build release gates, confidence checks, and operational controls that keep LLM systems useful in production.
Course goal
Ship one LLM workflow with clearer evals, safety controls, and escalation paths.
Built for staff engineers, platform teams, product and operations leads. Work through the modules in order if you want the full picture, or jump directly to the lesson that matches the job in front of you right now.
Evaluation playbook for LLM applications
Use task-specific evals, regression datasets, and release thresholds instead of ad hoc spot checking.
LLM safety, policy enforcement, and confidence gating
Add policy checks, refusal handling, and confidence-based routing so automation stays within acceptable risk boundaries.
Human-in-the-loop review and confidence gates
Define when automation should continue, when it should pause for review, and when the workflow should escalate.
When not to use an LLM
Reject weak use cases earlier by comparing LLMs against rules, search, deterministic logic, and traditional ML.