What this prompt does
This prompt uses Gemini to analyze an uploaded [document_type] and extract structured data. It identifies the document type and structure, extracts your [fields_to_extract], outputs them as [output_format] with consistent field naming, handles [edge_cases], attaches a confidence score to each field, flags ambiguous values, validates against [validation_rules], and produces a processing summary with total fields and success rate. Across multiple documents it keeps a consistent schema, and it returns the extraction logic as reusable [code_language] code for batch processing.
The structure works because downstream systems break on silent bad values. Confidence scores and validation rules are baked in from the start, so an unreadable amount or a malformed date gets flagged rather than passed along as clean data. Naming your exact [fields_to_extract] and [validation_rules] lets you prototype the extraction schema here before wiring it into a real batch job, where consistency across documents matters most.
When to use it
- You're building a pipeline that turns invoices, receipts, or forms into structured data.
- You need confidence scores so low-quality extractions can be routed for review.
- You want validation (ISO dates, decimal amounts, required fields) enforced at extraction time.
- You're prototyping the extraction schema before committing to a batch job.
- You're processing many documents and need one consistent schema across all of them.
- You want reusable
[code_language]code, not just a one-off manual extraction.
Example output
Expect structured output in [output_format] containing your [fields_to_extract], each with a confidence score, ambiguous values flagged, and a processing summary reporting field counts and success rate. Alongside it, you get [code_language] extraction logic you can run over a folder of documents while keeping the schema consistent. Edge cases like poor scans or multiple currencies are handled or explicitly flagged rather than crashing the run.
Pro tips
- List
[fields_to_extract]precisely with the names you want downstream; vague field requests produce inconsistent keys that break your consuming code. - Define
[validation_rules]strictly — ISO dates, decimal amounts, required-field presence — so invalid extractions are caught at the source, not three systems later. - Use the confidence scores to route low-confidence extractions to human review instead of trusting every field equally.
- Name your real
[edge_cases](handwriting, poor scans, mixed currencies) so the prompt accounts for them rather than assuming clean inputs. - Keep
[output_format]consistent across the batch; switching shapes mid-run is a common cause of downstream parsing failures. - Treat the generated
[code_language]code as a prototype and add retries and error logging before running it over a large document set.
