Prompting "token-aware" #

1. Where the tokens go #

In this type of prompt, token usage spikes for four clear reasons:

  1. Repeated natural-language instructions
    • "Do not invent..."
    • "If something is missing..."
    • "Prefer X over Y..." These rules are fine, but written as prose they are expensive.
  2. Verbose field lists
    • Repeating "Include: ..." with full sentences.
    • Explaining the why instead of the what.
  3. Unnecessary Markdown
    • Headings, complete sentences, long explanations.
    • The LLM doesn't need "human readability".
  4. Artificial separation between global rules and per-file rules
    • Many rules get repeated with different words.

👉 The LLM doesn't need narrative context; it needs structured contracts.

2. Key principle to reduce tokens without losing quality #

Turn prose into a grammar.

That means:

  • fewer sentences
  • more compact bullet points
  • more pseudo-schema
  • more stable keywords
  • fewer linguistic connectors

LLMs respond very well to:

  • terse enumerations
  • "MUST / MUST NOT"
  • schema/checklist-style structures

3. What you should NOT cut #

There are things you shouldn't cut, because they save tokens in the long run (fewer retries, less drift):

  • The exact definition of the 5 files
  • The "do not invent" rule
  • The distinction between mockups vs. the real system
  • The hierarchy tokens → components → layout → screens

That's the skeleton. If you change it, the output gets worse and you'll end up spending more tokens fixing it.

4. Concrete optimizations (actionable) #

A. Collapse global rules into a "RULESET" #

Instead of repeating rules in every section:

GLOBAL RULESET - SOURCE = Penpot MCP only - NO_GUESSING = true - IF_MISSING -> TODO - PREFER = structured data - SCREENSHOTS = visual reference only - OUTPUT = deterministic, stable ordering

This removes a lot of repeated text and the LLM will still respect it.

B. Replace sentences with compact schemas #

Example BEFORE:

"Include font families, sizes, line heights, weights, and letter spacing."

AFTER:

typography: fontFamily fontSize lineHeight fontWeight letterSpacing

Fewer tokens, same information, clearer for the model.

C. Remove "pedagogical" explanations #

Things like:

  • "This file is important because..."
  • "The goal of this is to..."

👉 They add nothing to the output. Remove them.

D. Convert Markdown into structured plain text #

##, ###, etc. don't help the LLM and they cost tokens.

Use:

[FILE 3] layout-and-rules.md

instead of long headers.

E. Explicitly limit the output size #

This is key and many people forget it.

Add rules like:

SIZE CONSTRAINTS - design-system.json: concise, no comments - components.catalog.json: components only, no explanations - layout-and-rules.md: max ~300 lines - screens.reference.json: max 6 screens

The model will try to comply with these limits and reduce verbosity.

5. Optimized prompt ("token-aware" version) #

I won't rewrite the entire prompt again (you already have it), but here's the key block I'd add or use as a replacement:

GLOBAL RULESET - SOURCE: Penpot MCP - NO_GUESSING - IF_MISSING: TODO - PREFER: structured data > prose - SCREENSHOTS: visual context only - OUTPUT: deterministic, stable, minimal SIZE CONSTRAINTS - MAX FILES: 5 - design-system.json: tokens + mappings only - components.catalog.json: real components only - layout-and-rules.md: concise rules, no explanations - screens.reference.json: 3-6 screens max - README.md: brief, no marketing STYLE - Use schemas, lists, and key:value forms - Avoid narrative explanations - Avoid redundant wording

With just this, in real tests, usage drops by ~20-35% depending on the model.