Descriptions:
Stephanie Nyarko presents a structured system for cutting Claude token consumption by 50–70%, arguing that most published advice targets minor savings while ignoring the dominant cost driver: uncontrolled output length. Claude defaults to verbose responses as a helpfulness heuristic, and Nyarko demonstrates that adding simple output constraints — “five bullet points, under 120 words” — can halve token usage on the same task with no quality loss. A landing page analysis prompt that would return 300–500 words unconstrained drops to a fraction of that with explicit format and length instructions.
The video covers three primary levers in order of impact. First, output constraints — framing prompts with explicit structure and word limits. Second, PDF handling — rather than uploading full documents and letting Claude process every header and filler page, Nyarko strips PDFs down to only the relevant sections before uploading, effectively building a lightweight retrieval layer manually. Third, model routing — using Claude Sonnet as the default for the majority of tasks, escalating to Opus only for genuinely complex reasoning, and switching to Haiku for speed-sensitive or simple queries. Using Opus universally, she notes, is “like using a supercomputer to open a calculator.”
A final section addresses prompt structure, explaining that vague inputs force Claude to spend tokens interpreting intent and selecting a response format — overhead eliminated by writing specific, constrained prompts. The framework is presented as a compounding system rather than a set of one-off tips, applicable across both API usage and Claude.ai subscription plans.
📺 Source: Stephanie Nyarko · Published April 20, 2026
🏷️ Format: Tutorial Demo
