Mechanical Scaffolding

Pay for structure once, pay for content every time.

The Pattern

Separate what is REPEATABLE (structure, format, fields, validation) from what is VARIABLE (context, content, specifics). Build the scaffold in code. Let the LLM fill it.

When to Use

• You explain the same format repeatedly
• Output shape is predictable across uses
• Validation rules exist and can be encoded
• You copy-paste between prompts

Implementation

Without scaffolding (every prompt):

Create a Jira ticket for this bug. Include:
- Summary (under 80 chars)
- Description with context
- Acceptance criteria as checkboxes
- Priority suggestion
- Labels

The bug is: users can't log in after password reset...

Token cost: ~150 tokens for structure + content tokens

With scaffolding (code handles structure):

# scaffold.py (runs locally, zero tokens)
def format_ticket(summary, description, criteria, priority, labels):
    return {
        "fields": {
            "summary": summary[:80],
            "description": format_adf(description),
            "customfield_acceptance": criteria,
            ...
        }
    }

# LLM prompt (each use)
Given this bug report, extract:
- one-line summary
- technical description
- acceptance criteria (list)
- suggested priority

Bug: users can't log in after password reset...

Token cost: ~50 tokens for extraction + content tokens

Savings: 60%+ on structure tokens, compounding across every use.

Examples

• Jira tickets: fields, formatting, ADF structure
• PR bodies: sections, checklists, evidence tables
• Status reports: headers, bullet constraints, recipient targeting
• Code reviews: checklist structure, severity levels
• Release notes: categorization, formatting, audience

When NOT to Use

• One-off tasks (no repetition to amortize)
• Exploratory work where format is unknown
• Tasks where the LLM should invent the structure

Tips

• Start with the hardest constraint (the part you always have to re-explain)
• Move validation to the scaffold, not the prompt
• Version your scaffolds like code
• Measure token savings to prove ROI

Related Patterns

• Structured Output: the schema; scaffolding is the mechanism
• Pipeline Orchestration: scaffolds often form pipeline stages
• Discovery Propagation: scaffold improvements propagate to all uses