Be the person who can answer “how do we know this AI work is good?”
Designed for product professionals, this 60-minute, self-paced course equips you to establish clear criteria for quality in AI behavior, identify actual output failures, and confidently justify your evaluation when questioned.
Why 91% Satisfaction Isn’t Enough: The Hidden Cost of Blind Spots
Imagine your AI product receives a 91 percent satisfaction rating.
If you cannot pinpoint who evaluated it, how many responses were actually reviewed, or who examined the remainder, that knowledge gap is a blind spot worth exploring.
Relying on “that looks right” is natural when clear standards for quality are missing — especially given how convincingly modern AI writes and builds. Consequently, quality standards are decided arbitrarily by whichever voice carries the most authority. The exact same output is approved one day and challenged the next, leaving a process that cannot be handed off to new team members or defended to executive leadership.
What you’ll learn
Evaluate single outputs and substantiate your decisions.
Utilizing four key criteria — role, context, goal, and labels — you will transform subjective reactions into documented, justifiable verdicts. You will categorize tasks using a two-variable framework based on error impact and reversibility, clarifying why seemingly similar scenarios, like parking inquiries versus benefits enrollment, carry distinct risk profiles.
Uncover unlisted failure modes.
Apply a four-part diagnostic framework across twelve real-world samples to spot edge cases that conventional criteria miss. This includes blind spots that manual review cannot detect — such as identical inquiries submitted simultaneously yielding divergent, yet individually valid and accurate, responses.
Construct automated, repeatable evaluation workflows.
Develop evaluations using six core elements: role, context, goal, labels, threshold, and cadence. Defining explicit thresholds converts raw scores into actionable decisions, while establishing cadence embeds evaluation into continuous operations.
Align verification methods with risk severity.
Deploy source verification, automated evaluations, and human-in-the-loop oversight according to established risk tiers. Ensure high-risk tiers mandate mandatory human review regardless of scale, safeguarding critical paths against volume-driven compromises.
Produce a practical, deployable asset.
Draft the AI Quality Playbook containing operational standards for your AI capabilities — including risk classification, escalation protocols, review accountability, and monitoring schedules — alongside an actionable Play. Formulate this document as an evidence-backed proposal ready for executive or policy-owner review.
Who should take this course
You manage an active AI feature.
Your current metrics rely on customer satisfaction, cost per response, and an absence of complaints. Although periodic reviews leave you uneasy, you lack the formal framework and terminology needed to define quality standards.
You oversee high-stakes AI deployment.
You are evaluating or approving critical AI behaviors without a clear mechanism to detect failures. You require a documented framework to justify conditional approvals, rejections, or launches.
How the course works
Enroll to get instant access at $149.
Start on your own schedule — no cohort, no deadlines, no time pressure.
Complete the course.
This 60-minute course spans four interactive lessons, with over half the time dedicated to hands-on practice rather than passive viewing — totaling roughly eight minutes of video. Right from the start, you select your environment: evaluate actual output from your own product, or work with a provided case study on Ridgeline Health, a nonprofit health system complete with a month of simulated output and twelve unreviewed answers. Whichever option you pick applies across all lessons as you progressively construct your custom Playbook.
Apply it Monday morning.
By the end, you will have completed your AI Quality Playbook. Your immediate next action: gather and review ten actual outputs from another behavior under your responsibility. Spending an afternoon doing this establishes you as the sole person in the room who has directly inspected the work. Since the underlying Rules are already set, executing the second Play requires only about an hour.
AI product fluency is the new professional standard
Automated evaluation platforms can score your outputs, but they cannot judge whether your quality criteria are correct. Only you understand the true cost of an error in your product and how difficult it is to remediate. Defining those boundaries remains your responsibility.
While engineering can automate the validation tests, the owner of the feature must establish what the product is permitted to communicate. Documenting these standards ensures you always have a defensible, clear answer when questioned about quality.
