Table Parsing Prompt AI Prompt

Design prompts that extract structured data from tables in various formats — HTML, Markdown, PDF text, and ASCII.

Tables from documents are often the richest data source but are structurally complex. LLMs can parse them, but need explicit instructions to do so reliably.

1. Table parsing challenges:
   - Multi-row headers (the column meaning is in 2 rows, not 1)
   - Merged cells (a cell spans multiple rows or columns)
   - Implicit structure (blank cells mean 'same as above')
   - Footnotes that modify cell values (values marked with * have different meaning)
   - Mixed data types in the same column

2. Table parsing prompt structure:

   Step 1 — Table understanding:
   'First, describe the structure of this table: how many rows and columns, what the headers mean, and any structural complexity (merged cells, multi-row headers, footnotes).'

   Step 2 — Header normalization:
   'List the column headers as they will appear in the output. If headers span multiple rows, combine them into a single descriptive name. Example: a table with "Revenue" in row 1 and "Q1 | Q2 | Q3" in row 2 produces columns: revenue_q1, revenue_q2, revenue_q3.'

   Step 3 — Row extraction:
   'Extract each data row as a JSON object. Resolve all implicit structure: fill in blank cells with the value from the cell above. Handle footnotes: if a cell has a footnote marker, include a footnote_[column] field with the footnote text.'

   Step 4 — Output:
   'Return a JSON array of objects, one per data row (excluding headers). Column names must match Step 2.'

3. Format-specific instructions:

   HTML tables:
   'Parse the <table> element. Handle colspan and rowspan attributes to correctly assign values to cells.'

   Markdown tables:
   'Parse the pipe-delimited table. The first row after |---|---| is the header. Each subsequent row is a data row.'

   PDF extracted text (hardest):
   'The table has been extracted from a PDF and may have alignment artifacts. Use column position context to assign values to the correct column even if whitespace is irregular.'

4. Validation:
   - After extraction: 'Verify that the number of values in each row matches the number of headers. Flag any row with a mismatch.'

Return: parsing prompts for each format, a test with a complex table (merged cells, footnotes), expected JSON output, and validation code.