AI systems don't produce consistent responses to identical prompts, shifting focus to reliable delivery for user safety. Quality data is essential; designers must ensure accuracy, completeness, consistency, freshness, and uniqueness in input data. Effective design anticipates diverse outputs and user engagements, requiring clear specification of answer formats and addressing scenarios with low confidence or poor responses. Operational constraints like latency and cost are critical; design guidelines should prepare users for delays and privacy challenges while maintaining effective product functionality.
AI doesn't operate in a deterministic manner; varying prompts can yield different results. Focus shifts to reliably and safely delivering AI outputs for user trust.
Data quality is crucial for AI efficacy. Designers must ensure data accuracy, completeness, consistency, freshness, and uniqueness to enhance AI performance.
Effective design for AI involves anticipating outputs and user interactions. Designers need to define answer formats and map states like low confidence or poor responses.
Consider operational constraints like latency and cost during design. Design must guide users through potential delays and privacy concerns while preserving operational integrity.
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