Google Updated Generative-AI-Leader Exam Questions and Answers by saint

Prompt engineering, especially techniques like few-shot prompting (providing examples of desired input-output pairs), is crucial for guiding a generative AI model to understand context and generate relevant, human-like responses. Limiting data or using strict keyword matching would severely restrict the chatbot's conversational ability, and lowering temperature makes responses less creative, not necessarily more understanding.

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When AI struggles with nuances like sarcasm or satire, human oversight is often the most effective solution. A human-in-the-loop (HITL) process allows human reviewers to check, correct, and refine AI-generated content before it is published, ensuring accuracy and appropriateness, especially for sensitive or complex language.

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Gemini, as a large language model, excels at flexible content generation, summarization, understanding, and inference. However, it is not designed for deterministic, rule-based decision-making that requires absolute consistency and adherence to strict, predefined criteria, as is common in highly regulated financial systems like loan approvals. Such systems typically require traditional programming logic or specific rule engines for auditable and consistent outcomes.

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