Data Entry & Extraction

One of AI's genuine superpowers: turning messy, unstructured text into clean, structured data. Tasks that used to take an intern all day now take 30 seconds.

Extracting Data from Text

The basic pattern — tell AI what fields you want and what format to use:

"Extract the following information from this email/document and format as JSON:

- Company name

- Contact person

- Email address

- Phone number

- Requested service

- Budget mentioned

- Timeline

If any field is not found, use 'N/A'.

If you're unsure about an extraction, add a confidence note.

Document: [paste text]"

Invoice Processing

"Extract line items from this invoice text and format as a table:

| Item | Quantity | Unit Price | Total |

Also extract: Invoice number, date, vendor name, subtotal, tax, and grand total.

Double-check that line items add up to the subtotal. Flag any discrepancies.

Invoice: [paste invoice text]"

The "double-check the math" instruction is critical — AI sometimes misreads numbers.

Business Card / Contact Parsing

"Parse these business card details into a contact database format:

[paste business card text or multiple cards]

Format: CSV with columns: First Name, Last Name, Title, Company, Email, Phone, Address, LinkedIn URL

If a card has multiple phone numbers, use the mobile number for the Phone column and note others in a Notes column."

The Batch Processing Pattern

For processing many items at once, always provide an example first:

"Process each entry below. For each one, extract: name, date, amount, and category.

Example:

Input: 'Paid $450 to ABC Plumbing on March 15 for bathroom repair'

Output: Name: ABC Plumbing | Date: March 15 | Amount: $450 | Category: Home Repair

Now process these entries:

1. 'Bought $89 of office supplies at Staples on Tuesday'

2. 'Monthly Spotify subscription $14.99 charged Jan 1'

3. 'Dinner with client Sarah at Olive Garden $127.50 on 3/10'

4. 'Paid quarterly insurance premium $2,400 to State Farm'

5. 'Gas station fill-up $62.18 Shell on Highway 101'"

Cleaning Messy Data

Real-world data is never clean. AI handles the mess:

"This spreadsheet data has inconsistencies. Standardize it:

- Names: First Last format, proper capitalization

- Phone numbers: (XXX) XXX-XXXX format

- Addresses: Full format with ZIP code

- Dates: YYYY-MM-DD format

- Remove duplicates (keep the most complete entry)

Data: [paste messy data]"

Transforming Between Formats

"Convert this data from [format A] to [format B]:

- JSON to CSV

- Email thread to structured table

- Meeting notes to Jira tickets

- Resume text to database fields

[paste data]"

Tips for Accurate Extraction

1. 1.Always specify the output format (JSON, CSV, table, etc.) — ambiguity kills accuracy
2. 2.Provide one example for complex extractions — AI learns your expectations
3. 3.Ask AI to flag uncertain extractions with a confidence indicator
4. 4.Always verify extracted numbers — AI occasionally misreads or transposes digits
5. 5.For critical data, ask AI to double-check itself: "Review your extraction above. Did you miss anything or make any errors?"
6. 6.Chunk large datasets — Process 20-50 items at a time, not 500