Procurement Document Generation

Procurement Document Generation

Overview

Generate structured procurement documents using a template engine pattern: templates with placeholders are populated with validated structured data from procurement systems (ERP, supplier database, order management). Supports multi-format export (PDF, DOCX, structured JSON, Excel) with provenance tracking on all populated values.

Announce at start: "I'm using the procurement-document-generation skill to create a procurement document from template with structured data injection."

When to Use This Skill

Trigger Conditions:

• Generating a Purchase Order from approved requisition
• Creating an RFQ for supplier solicitation
• Generating a contract from negotiated terms
• Creating a supplier evaluation report
• Generating a Non-Conformance Report (NCR)
• Tender document generation
• Contract close-out documentation

Prerequisites:

• Template exists for target document type
• Source data extracted and validated (via procurement-data-extraction)
• Data provenance tags present on all source values
• Document recipient/audience identified

Step-by-Step Procedure

Step 1: Template Selection

Select appropriate template based on document type and complexity:

Step 2: Data Assembly

Assemble data from validated source systems:

Data injection format:

{
  "template_name": "purchase_order_complex",
  "data_source": "validated_requisition_R-2026-0142",
  "injection_points": {
    "po_number": "PO-2026-0089",
    "supplier_name": "SteelWorks Ltd",
    "supplier_address": "123 Industrial Way, Johannesburg",
    "order_date": "2026-03-31",
    "delivery_date": "2026-07-15",
    "line_items": [
      {
        "item_code": "ST-W310-001",
        "description": "Structural W310 sections, Grade S355JR",
        "quantity": 340,
        "uom": "tonnes",
        "unit_price": 18500,
        "currency": "ZAR",
        "total_line": 6290000
      }
    ],
    "incoterms": "DAP Site C, Gate 3",
    "payment_terms": "30% advance, 70% on delivery",
    "approved_by": "John Smith, Procurement Manager"
  }
}

Step 3: Template Rendering

Render template with injected data:

Template Design Principles:

1. Structured Data Injection, Not Raw Generation — All values come from validated sources, not LLM-generated text
2. Provenance Tracking — Every injected value carries source reference (e.g., {source: "quotation_Q-2026-142", item: "concrete_C30"})
3. Conditional Logic — Optional sections added/omitted based on data availability (e.g., "if no warranty period, omit warranty clause")
4. Regulatory Accuracy — Current codes, standards, and regulations referenced
5. Multi-Language Support — Documents support all languages represented in supply chain
6. Human Readable with Data Export — Documents exportable as structured data for analytics

Rendering Pipeline:

TEMPLATE SELECTION (document type, complexity)
    ↓
DATA INJECTION (structured data sources → placeholder mapping)
    ↓
CONDITIONAL LOGIC (add/omit sections based on data)
    ↓
QUALITY VALIDATION (completeness, accuracy, consistency)
    ↓
DRAFT DOCUMENT (formatted output)
    ↓
PROVENANCE TAGGING (all values tagged with source)
    ↓
OUTPUT GENERATION (PDF, DOCX, JSON, Excel)

Step 4: Quality Validation

Validate generated document before output:

Step 5: Multi-Format Export

Generate output in requested formats:

Step 6: Version Control & Distribution

Manage document lifecycle:

1. Version Number — Generate version (v1.0 for first, v1.1 for revision)
2. Audit Trail — Log document generation: who, when, data sources, template version
3. Distribution — Route to stakeholders per approval workflow
4. Archival — Store in document management system with metadata tags

Success Criteria

• Correct template selected for document type and complexity
• All data injected from validated sources (no fabricated values)
• All calculations correct (totals, taxes, currency conversions)
• All cross-references valid and resolvable
• Document passes all quality validation checks
• Provenance tags present on all populated values
• Output generated in requested format(s)
• Document logged to audit trail with version control

Common Pitfalls

1. Generating Text for Data Fields — Never let the LLM generate numbers for quantities, prices, or dates. All values must come from validated source data.
2. Missing Provenance — If a value doesn't have a source reference, the document cannot be used for audit, dispute resolution, or quality checks.
3. Ignoring Conditional Logic — Not all documents need all sections. Use conditional logic to omit sections that don't apply (e.g., no warranty for off-the-shelf purchases).
4. Skipping Quality Validation — Always run completeness and accuracy checks before output. A document with errors distributed to a supplier damages credibility and causes rework.
5. Forgetting Version Control — Always version documents. An unversioned revision creates confusion about which version is current.

Cross-References

Related Skills

• procurement-data-extraction — Provides validated source data for injection
• procurement-order-management — Consumes generated PO documents
• sow-generation — Specialized document generation for scope of work
• procurement-compliance — Validates document compliance requirements

Related Agents

• Contract Administration Specialist (DomainForge) — Contract document generation
• Procurement Strategy Specialist (DomainForge) — RFQ and tender document generation
• Procurement Analytics Specialist (DomainForge) — Document export for analytics

Example Usage

Scenario: Generate Purchase Order PO-2026-0089 for 340t structural steel

1. Template: Select purchase_order_complex template (complexity: standard with technical specs)
2. Data Assembly: Load approved requisition R-2026-0142, quotation Q-2026-142
3. Data Injection: Populate all template fields from validated sources
4. Quality Validation: Verify calculations (340t × R18,500 = R6,290,000), check all references
5. Output: Generate PDF for supplier distribution, JSON for system integration
6. Version Control: Log as v1.0, route for approval, archive with metadata

Performance Metrics

Target Performance:

• Template rendering accuracy: >99% (all placeholders correctly populated)
• Data accuracy: >99.9% (exact match with source data)
• Document generation time: <10 seconds (simple), <60 seconds (complex with multiple appendices)
• Review rejection rate: <5% of documents require revision after generation