Using Generative AI to Clean a CSV File

With generative AI, you can quickly clean and process massive lead lists in minutes.

But before you can do this, you’ll need to do a bit of prep work.

Don’t worry though. The time you invest now will pay huge dividends in the long run.

After all, manually cleaning a lead list can take you hours, and you could still have mistakes and typos at the end of the day. 

At AiSDR, we’ve delegated CSV cleaning completely to generative AI. In addition to saving us considerable time that we repurpose for product development, we also cut down on the number of innocent mistakes and typos.

TLDR: 

• The goal: Clean a messy CSV file
• The tactic: Set up generative AI to clean the CSV file
• The result: Get a CSV that contains your data in a clean and readable format

What is generative AI?

Generative AI is an advanced tool that takes your instructions and turns them into structured, corrected, or enhanced data. Instead of using manual formulas or filters, it follows prompts written in natural language to understand what you want and apply it across your file.

Step 1: Design a Prompt for Generative AI

This is the hardest and most time intensive step as it will require a bit of testing, as well as trial and error.

Essentially, your prompt will instruct generative AI about what to do and what not to do.

For example:

You are a csv parser and cleaner that follows these rules:

• Never change the values of fields
• Output columns must be in the following order: First name, Last name, Email
• No other columns must be present

Here we’ve told generative AI:

• What it’s doing (i.e. “You’re a csv parser and cleaner”)
• Rules (i.e. “Never change values”)
• Expected output (i.e. “Output columns must be in the following order”)

This is just the start of the prompt though. You’ll need to add additional instructions based on your testing results.

Step 2: Feed Common Mistakes and Typos into Generative AI

People make mistakes, which means data entry can get a bit messy.

If you’ve collected enough lead data and consistently worked with CSVs, chances are you’ve noticed certain errors occur with some degree of regularity.

Consider these examples:

• John Smith, Ph.D.
• Nike, Inc.
• 11/12/2024

Each example is written correctly. However, each example can cause an error during CSV import and cleaning.

CSV files separate values using commas, so generative AI will misread the info and cause “Ph.D.” and “Inc.” to get entered into the wrong column. You can tell generative AI that these cases can happen and what to do if it comes across them.

As for the date, it will produce an error depending on your location. If you’re in the United States, then “11/12” means “November 12”, but if you’re in Europe, “11/12” is “December 11”. Accordingly, you’ll want to specify to generative AI your expected date format.

Step 3: Provide an Example of a Good CSV

Generative AI models function best when they’re able to work off of an example.

If you already have a CSV lead list, then you’re set and you can use it as the example. Alternatively, you can create a small CSV with 5-10 rows of imaginary lead data. This should be enough to get the point across to generative AI.

However, keep in mind that the example should be “perfect”. Otherwise, generative AI could build errors into future files.

Why AI is better than Excel or manual CSV editing

With Excel, you often need formulas, filters, or scripts to clean and format data. It’s easy to miss edge cases or create errors when working manually. Generative AI lets you describe what you want in plain language and applies it consistently across the dataset. It is faster, more scalable, and more flexible than manual editing.

The Result

If everything works as expected, you should be able to:

• Upload any CSV file containing lead data to generative AI
• Have generative AI clean the file for you based on your criteria
• Download the resulting file

FAQ

What are some common errors found in CSV files?

Things like commas in company names, mismatched formats for dates, state abbreviations treated as separate fields, and symbols (like apostrophes) causing parsing errors.

Does AI need to be trained using examples?

Yes. AI performs best when given a clean example. This tells it how to replicate structure, formatting, and field logic throughout the rest of the file.

Is it safe to use AI to work with personal data in CSV files?

As long as you control what data goes into the prompt and ensure you’re not sharing sensitive details in public tools, using AI to work with CSV files is safe.