The Economics of Tokens
Token pricing looks cheap until your knowledge base has 50,000 pages. Most teams burn money by feeding raw PDFs or DOCX exports directly into prompts or embedding jobs. Those formats carry hidden markup, repeated headers, and layout artifacts that inflate tokens by up to 70%.
Markdown strips those artifacts, compresses structure, and gives you clean semantic cues. Couple Markdown with deduplication and caching and you can reduce spend dramatically in both retrieval and generation workloads.
Token Reduction Playbook
Pre-processing
- Convert every source file to Markdown via MDConvert.
- Normalize headings, remove footers, and collapse whitespace.
- Deduplicate repeated sections with hash-based comparisons.
- Attach metadata blocks for caching layers.
LLM Interaction
- Chunk to 400–600 tokens with semantic boundaries.
- Cache embeddings and prompt prefixes keyed by hash.
- Use Markdown tables instead of prose for structured data.
- Return citations so you never re-query unnecessary context.
Savings example (500 PDFs, ~200 pages each)
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Raw PDF text: ~480M tokens / month
Markdown conversion: ~260M tokens / month
Chunk caching: ~180M tokens / month
Total reduction: 62% (≈ $18k → $6.8k @ GPT-4o mini embeddings)
Implementation Checklist
- Instrument expenses: Capture tokens per workflow (ingestion, retrieval, generation) and tag by team.
- Adopt Markdown as source of truth: Everything else derives from the Markdown repository.
- Enforce lint rules: Headings, glossary expansions, and table formatting avoid prompt waste.
- Automate caching: Store embeddings by document hash and reuse previously chunked Markdown.
- Report savings: Share monthly dashboards showing token deltas and ROI back to stakeholders.