Output Limitations, Evaluation and Feedback
Improving GenAI output starts with understanding quality failures, hallucination, bias, safety issues, evaluation data, feedback loops, and review gates.
How to study for Google Cloud Generative AI Leader
Treat each item as a leadership decision: define business value, match Google Cloud capabilities, improve output quality, then govern rollout responsibly.
Core concepts
Concept 1
Evaluation should use representative examples, expected outputs, scoring criteria, human review, and production feedback.
Exam cue: Use test examples when comparing prompt, model, or grounding changes.
Concept 2
Common output risks include hallucination, incomplete answers, unsafe content, bias, data leakage, poor tone, and unsupported reasoning.
Exam cue: Investigate retrieval quality when factual answers miss available source content.
Concept 3
Feedback loops should separate user preference, factual correction, safety incidents, and model or retrieval improvement signals.
Exam cue: Separate user satisfaction from factual accuracy and safety compliance.
Risk pitfalls and guardrails
Relying on a few demos instead of representative evaluation.
Guardrail: Avoid choosing a model before proving business value, data readiness, evaluation criteria, and responsible AI controls.
Optimizing for fluent tone while ignoring accuracy.
Guardrail: Avoid choosing a model before proving business value, data readiness, evaluation criteria, and responsible AI controls.
Mixing all feedback into one metric that hides safety or factual defects.
Guardrail: Avoid choosing a model before proving business value, data readiness, evaluation criteria, and responsible AI controls.
Memory anchors
Evaluation Set
An evaluation set is a representative collection of inputs, expected behavior, and scoring criteria.
Quality Rubric
A quality rubric defines how outputs are judged for accuracy, completeness, safety, tone, and usefulness.
Feedback Loop
A feedback loop captures user, reviewer, and production signals to improve prompts, retrieval, or controls.
Factuality
Factuality measures whether an output is supported by correct source information.
Safety Defect
A safety defect is output that violates harm, policy, compliance, confidentiality, or responsible AI rules.
Human Rating
Human rating uses reviewers to judge quality dimensions that automated checks may miss.
Regression Test
A regression test ensures a prompt, retrieval, or model change does not break known behavior.
Production Signal
A production signal measures real usage, failures, latency, cost, escalation, or user outcomes.
Checkpoint rule
Do the check-up only after you can summarize each concept in one sentence and identify one dangerous pitfall from memory.
Knowledge Check (after reading)
Short check-up to confirm understanding of this module.
Check-up Questions
A model answers a question about a regulation enacted after its training data cutoff. Why is the ungrounded response especially unreliable?
An assistant cites a policy clause that sounds realistic but does not exist in any approved document. Which generative AI limitation does this illustrate?
Answer all questions to submit.
Next step personalized recommendations
Continue learning
Move forward only after this module is stable.
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