About this course
Reliability and governance are not just quality concerns — they are cost concerns. Every failed request, every blind retry, every rollout without evaluation gates burns money. A system that knows when to stop, when to escalate, and when not to use an LLM at all is cheaper to operate than one that tries everything. This course teaches the operational controls that keep LLM systems useful and economical in production.
What you will learn
- How to build task-specific evals that catch regressions before rollout
- How policy enforcement and confidence gating prevent waste from low-quality outputs
- When to pause for human review vs when to let automation continue
- How to identify use cases where an LLM is the wrong tool entirely
Why this belongs in AI Cost Academy
Governance controls reduce failed requests, rollout-driven cost spikes, and review burden — all of which affect unit economics. A system that fails gracefully costs less than one that retries blindly.
How to use this course: Work through the modules in order for the full picture, or jump to the lesson that matches the problem in front of you right now. Each module is a standalone read — estimated total time is 39 minutes.
Course modules
4 lessons · 39 min total read time
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.
Frequently asked questions
Answers to the questions teams ask before starting this course.
How is LLM reliability a cost issue?
Every failed request, blind retry, and rollout without evaluation gates burns money. A system that knows when to stop, when to escalate, and when not to use an LLM at all is cheaper to operate than one that retries blindly and ships regressions to production. Governance controls reduce failed requests, rollout-driven spikes, and review burden — all of which show up in unit economics.
What should an LLM evaluation cover before rollout?
A task-specific eval set with expected outputs, a regression dataset of cases you have broken before, and a release threshold the change must clear to ship. This turns "it looks better" into a gate that catches quality drops — and the cost spikes that follow retry storms and re-rollouts — before they reach production.
When should an LLM workflow escalate to a human?
Escalate on low confidence, high stakes, or policy-sensitive decisions. Define three states explicitly: continue automatically when confidence is high and the action is reversible, pause for human review when confidence is marginal or the action is costly, and hard-stop when the request violates policy. Confidence gating is what keeps automation inside acceptable risk and cost boundaries.
When should I not use an LLM at all?
When a rule, a database query, deterministic logic, or a traditional ML model solves the problem more cheaply and reliably. LLMs are the wrong tool for exact lookups, deterministic calculations, and high-volume decisions with clear rules. Rejecting weak use cases early is one of the largest and most overlooked cost savings available.
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