Generate a series of AI prompts designed to clean various datasets.  Each prompt should specify a common data cleaning task (e.g., handling missing values, removing duplicates, standardizing formats, identifying and correcting outliers, etc.) and provide instructions on how to adapt the prompt for different data types (e.g., numerical, categorical, textual) and structures (e.g., CSV, JSON, SQL database).  The output should be organized into sections based on the type of data cleaning task, with each section containing multiple prompts demonstrating different approaches and levels of complexity. Include examples of how the prompts should be used with different AI models (e.g., specifying the language model, providing example input data).  The prompts should be concise, clear, and easy to understand for both technical and non-technical users.  Focus on producing prompts that are robust and adaptable to various data cleaning scenarios.  The generated prompts should be suitable for use with large datasets and should consider potential challenges such as memory limitations and processing time. Aim for practical, immediately implementable prompts that can improve data quality in a variety of contexts.  Consider the potential for error handling within the prompts themselves. The final output should be formatted as a structured list with clear headings and subheadings, including examples for each prompt.