Data is the lynchpin of effective and responsible use of GenAI, highlighting the need for using clean, vetted, and current data to receive accurate responses. Many GenAI platforms might inadvertently access irrelevant or outdated data, leading to inaccurate outputs. The phrase 'garbage in, garbage out' aptly summarizes the importance of data quality.
For government agencies, data quality is a huge concern, especially when implementing AI initiatives. Agencies need the insight, direction, and confidence that only comes with current and actionable data, as the quality of data you input directly impacts the output.
Inaccurate data inputs could lead to hallucinations, instances where GenAI models confidently provide answers that appear correct but are actually wrong. Hallucinations are increasingly pervasive as organizations integrate GenAI into their workflows.
Having GenAI-ready data is essential to avoid creating a data 'Frankenstein,' which can arise from the use of dirty or unreliable data. Ensuring data hygiene is crucial for accurate outcomes.
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