AI is revolutionizing the way we handle data. Actionable insights can be generated faster than ever and AI-supported data preparation leads to improved data quality and less potential for human error. Data Management has never been easier.
Cleaning and preparing large datasets for analytics used to be reserved for skillful coders or data-savvy analysts. With AI, quick and efficient data cleaning is possible for anyone.
However, the value of AI depends on the input and it is no secret that the quality of results of AI-powered tools heavily depends on the quality of your prompts. Ultimately, what might stand between you and your perfect data foundation is knowing how to prompt properly.
In this blog, we’ll delve into how you can harness powerful data transformation tools to streamline your data cleaning processes and the best practices for data cleaning prompts.
What are AI prompts and how can they help with transforming data?
AI prompts are the instructions you give to AI models to perform specific tasks efficiently and accurately. The input you provide the AI model with is called a “prompt”. This could be a question, a command, or a statement that specifies what you want the AI to do or achieve. The AI model then processes your prompt using Natural Language Processing (NLP) and identifies key components such as intent, context, or entities. The AI model then generates a response based on the prompt.
For instance, if the prompt is a request for information, the AI searches its knowledge base to provide an accurate answer. If it's a command, the AI executes the specified task. AI models can refine their understanding and responses over time based on user feedback and additional data. Chances are, the more you use an AI tool, the better its output becomes over time.
In the area of data transformations, AI prompts can be used to instruct models to identify and correct errors, inconsistencies, and gaps in a dataset. You could also ask the AI model to merge data from various sources or add relevant information to a dataset based on certain indicators. For the sake of unifying a dataset, you might want to change date formats, country codes, or currencies. To protect privacy, avoid entering any sensitive personal information.
How to write effective AI prompts for data transformation
AI models are smart, but they can’t read your thoughts. So, make sure your prompts are clear and context-rich. As a rule of thumb, you can structure prompts as follows:
- Know your use case: What would you like to achieve?
- Give clear instructions: How should the task be approached? It can make sense to break down complex tasks into multiple instructions or steps.
- Provide context: Where does the data you want to transform sit?
Just like in regular face-to-face conversations, vague instructions lead to ambiguity and misunderstandings. The more context and clarity you provide in prompting, the more likely the AI model is to provide the output you’re looking for.
Example:
- Use case: I want to get rid of any duplicates in my dataset
- Prompt:
- "Remove all duplicates in the dataset" [use case]
- "based on the campaign IDs" [clear instruction]
- "in column D" [context]
- AI action: Remove duplicates based on campaign IDs
- Output: "Data cleaning complete: 50 duplicates removed”
Excellent prompts for AI-supported data transformations
The following are some key best practice prompts for data cleaning.
|
Use case
|
Prompt
|
Why this works
|
|
Basic data cleaning |
"Remove special characters from the Product Name column." | The prompt is straightforward and clear. |
|
Filtering values |
"Return only values which contain 'Mobile' in the Device column." | The prompt specifies both the filter criterion and the relevant column. |
|
Generating code |
"What Python expression can I use in addfieldx to calculate the difference in days between the Start Date and End Date columns?" | The prompt is clear about the columns involved and the desired calculation, leaving no room for interpretation. |
|
Formatting dates |
"I would like to convert my Date column format from DD-MM-YYYY to YYYY-MM-DD." | The prompt provides precise formatting instructions. |
|
Adding columns based on certain conditions |
"Add a new column called 'Channel type' that contains 'online' if values in the Channel column are 'Social Media', and 'offline' if values in the Channel column are 'TV'." | The prompt includes the new column's name and the conditions for its values. |
|
Advanced data cleaning |
"I want to:
1. Split the values in the 'Ad name' column into 4 separate columns, using the _ as the delimiter.
2. Remove empty rows from those 4 newly created columns."
|
The prompt breaks down the task into steps to ensure clarity and accuracy. |
How Adverity can help with transforming data
Adverity is the integrated data platform to help Marketing and Analyst teams build and maintain a reliable data foundation. At Adverity, you can create any combination of instructions to convert your data into the exact format you need with the Transformation Copilot.
The genAI-powered feature allows you to simply articulate how you want your data transformed in English, and you will instantly receive a detailed set of instructions. There are no technical limits on getting your data exactly how you want it with Adverity. You can create any combination of transformation instructions to convert your data into the exact format you need. This makes combining and unifying datasets from different data sources easy and manually combining Excel files obsolete.
Want to see how it works? Check out the video guide or take a digital platform tour. If you’re curious about other AI features in Adverity, be sure to visit our AI page.
Conclusion
In summary, crafting the right prompts is key to leveraging the full power of generative AI-powered features. The output of AI tools is only as good as your prompting.
By adhering to best practices—such as providing clear, detailed instructions and relevant context—you can leverage the full potential of AI-tools to streamline your data preparation tasks. As AI technology continues to evolve, the ability to effectively communicate with these tools will become increasingly vital, empowering users to achieve cleaner, more reliable datasets with ease.
