AI Complex Data Extraction & Transformation Prompt Generator

AI Complex Data Extraction & Transformation Prompt Generator — ChatGPT & Gemini (Lite, Safe Print v2)

AI Complex Data Extraction & Transformation Prompt Generator — ChatGPT & Gemini

ChatGPT · Gemini
How to use — Quick Guide
  1. Describe your source, record boundary, and sensitivity.
  2. Design your schema (field, type, required, regex/enums, examples).
  3. Pick optional presets (Instruction, Rules, Output, Closure).
  4. Set transforms, validation, and missing-data behavior.
  5. Click Generate Prompts → use Copy / Copy+Open / Download / Print buttons.

v2 note: meters now start at 0% when the form is empty (no false baselines).

A. Source & Context

B. Schema Designer

Types: string, number, integer, boolean, date, datetime, currency, enum, regex, array, object.

Field Name Type Required Regex (validation) Allowed Values (enum) Example Notes / Transform hint Del

C. Presets (Instruction, Rules, Output, Closure)

Presets combine with your schema, validation, and transforms for a complete, robust prompt.

D. Transforms & Validation

Based on source type, boundary strategy, and delimiter pattern.
Score: 0%
Counts fields, types, required flags, and validations (regex/enums/examples).
Score: 0%
Overall Robustness (avg): 0%

ChatGPT Prompt

Gemini Prompt

Heads-up: For currency/unit conversions, models estimate — validate with your systems.

FAQ — Complex Data Extraction & Transformation

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Who needs this generator?
  1. Operations & business analysts: Wrangle unstructured reports, emails, and logs into tidy tables for weekly KPIs, root-cause reviews, and vendor scorecards.
  2. Finance & procurement teams: Standardize invoices/POs across suppliers, convert currencies, normalize tax fields, and reconcile duplicates before import to ERP.
  3. E-commerce & product ops: Extract product specs from PDFs/HTML, map messy attributes to a canonical taxonomy, and generate clean CSV/JSON for catalog updates.
  4. Sales & marketing ops: Parse leads from emails/webhooks, validate UTM parameters, de-dupe contact lists, and enrich fields for CRM hygiene.
  5. Legal & compliance: Pull clauses, parties, dates, and obligations from contracts; flag missing required fields; mask PII; output audit-friendly tables.
  6. HR & talent teams: Normalize resumes or job posts into consistent schemas (skills, years, tools, certifications), ready for ATS ranking.
  7. Security & IT: Extract vulnerability IDs, severities, and patch status from advisories/tickets; output structured JSON for SIEM/CMDB.
  8. Research & data teams: Lift quantitative results from PDFs, lab notes, and scraped pages; convert units; validate types; export analysis-ready data.
  9. Healthcare & public sector (no PHI): Standardize codes, dates, and measurements from forms/reports while redacting identifiers and documenting uncertainty.
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What can you do with this prompt?
  1. Extract from messy sources
    • PDFs, HTML pages, emails, logs, CSV, JSON/NDJSON, mixed text.
    • Record boundary strategies (per line, per paragraph, table row, or custom delimiter/regex).
  2. Enforce a custom schema
    • Define fields, types (string/number/boolean/date/currency/enum/regex/array/object), and required flags.
    • Add regex and allowed values (enums) to stop junk before it enters your systems.
    • Include examples so the model understands format expectations.
  3. Transform & normalize
    • Convert dates (e.g., ISO 8601 → YYYY-MM-DD), currencies (to USD/EUR/GBP/INR/JPY), and units (kg/cm/L).
    • Compute derived fields (e.g., warranty months, subtotal + tax = total).
    • Map synonyms/aliases to your canonical taxonomy (e.g., "Grey", "Gray" → "Gray").
  4. Validate, deduplicate, and reconcile
    • Drop or mark records that fail validation.
    • Deduplicate by a primary key (invoice_number, SKU, ticket_id).
    • Keep the last/most complete instance and note conflicts.
  5. Control sensitivity & compliance
    • Redact or mask PII (emails, phones) unless explicitly required.
    • Add an extraction_notes field so the model logs uncertainty or edge cases.
  6. Choose your output and precision
    • JSON array, CSV/Markdown table, or YAML—pretty-printed if you like.
    • Strict types mode to prevent coercion (e.g., "N/A" won’t sneak into a number field).
  7. Build repeatable, auditable flows
    • Use presets (Instruction/Rules/Output/Closure) to standardize prompts across teams.
    • Keep an “Input Document” for each run: source description, schema, rules, and guardrails.
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Practical examples (copy-ready ideas)
  • Invoices (Finance): Extract invoice_number, invoice_date, supplier_name, line items, subtotal, tax_amount, total, and normalize currency to USD with 2-dp rounding.
  • Product catalogs (E-commerce): From spec sheets/HTML, pull sku, brand, model, dimensions_cm, weight_kg, materials[], and warranty_months.
  • Leads (Marketing): From inbound emails, capture full_name, company, email (masked or retained), interest_area (enum), utm_campaign, and country (ISO-2).
  • Contracts (Legal): Extract party_a, party_b, effective_date, term_months, termination_clause_present (bool), governing_law (enum), and clause references.
  • Vulnerabilities (Security): From advisories, pull cve_id, severity (P1–P3), affected_versions[], fix_available (bool), and release_date (YYYY-MM-DD).
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Why this matters
  • Consistency at scale: Every run uses the same schema and rules—no more ad-hoc copy/paste.
  • Lower cleanup cost: Regex, enums, and required fields stop bad data early.
  • Faster time-to-insight: Clean JSON/CSV/YAML drops directly into BI tools, CRMs, ERPs, or data pipelines.
  • Safer handling: Built-in PII controls and optional confidence notes help with audits and compliance.
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How to get the best results (quick tips)
  1. Start narrow: Define the few fields you truly need; expand later.
  2. Be explicit: Add regex/enums and examples for tricky fields.
  3. Set boundaries: Choose the right record strategy (per line/row/table/delimiter).
  4. Decide missing-data behavior: null vs drop vs add an error_note.
  5. Keep types strict for imports into databases/ERPs.
  6. Test on a small sample (use the Self-Test), then run on the full set.
  7. Log uncertainty: Turn on extraction_notes for edge cases and QA.
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