Stop Burning Money on Every LLM Call
Are you watching your OpenAI, Claude, or Gemini bill climb every month without understanding why? Most developers and builders pay 3–5x more than they need to — simply because no one taught them how tokens actually work. This course fixes that.
What You'll Learn:
We start from first principles. You'll learn exactly what a token is, how tokenizers break your text into billable units, and why two prompts that look identical can cost wildly different amounts. From there, we move into hands-on optimization — measuring token usage before you spend, trimming prompts without losing quality, and managing the context window so you never pay for bloat.
You'll learn how to count tokens with real tokenizer tools, rewrite verbose prompts into lean ones, apply summarization and truncation to long conversations, and cut costs at scale using caching, batching, and smart model routing. Every technique is shown with real before-and-after cost numbers so you can see the savings immediately.
Why This Course?
Everyone is learning to prompt. Almost no one is learning to prompt
efficiently
. As AI moves into production, token cost is becoming the difference between an app that's profitable and one that bleeds money. This course teaches you the optimization skills that keep your LLM projects sustainable at any scale.
Whether you're a developer, founder, automation builder, or indie hacker shipping AI features, these techniques will directly lower your costs and make your systems faster.
By the end, you'll be measuring, optimizing, and controlling token spend with confidence across any LLM provider.
No advanced math required. Just bring your API key and the willingness to stop overpaying.

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