Which task can be accomplished with the data cleaning capabilities of generative AI?

Generative AI models, specifically Large Language Models (LLMs), are highly effective atIdentifying inaccuracieswithin a dataset during the data cleaning phase. When provided with a dataset and a prompt to "check for consistency" or "identify anomalies," the AI can cross-reference the data points against its internal knowledge base or the logical rules established in the prompt. For example, if a list of "US States" includes "London," the AI can flag this as an inaccuracy.

This capability extends to identifying spelling errors, formatting inconsistencies (e.g., dates written in multiple formats), and logical contradictions. While AI can help in identifying bias (Option D), that is usually considered a higher-level "auditing" task rather than a standard "cleaning" task. Identifying inaccuracies is a foundational step in the data pipeline; by cleaning the data first, the user ensures that any subsequent analysis or "conclusion drawing" (Option C) is based on high-quality, reliable information. In prompt engineering, this is often performed using the "Self-Correction" or "Reviewer" pattern, where one prompt generates data and a second prompt is used specifically to identify and fix any factual or structural inaccuracies within that output.