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Master LLM Token Optimization: Cut Your AI Costs by 70%
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Master LLM Token Optimization: Cut Your AI Costs by 70%

Reduce token usage, slash API bills & optimize prompts for GPT, Claude & Gemini in production
New
Created byIng.Seif | Europe Innovation
Last updated 7/2026
English
English [Auto],

What you'll learn

  • Understand what tokens are and how tokenizers turn text into billable units
  • Measure and count token usage accurately before spending on any LLM call
  • Write lean, optimized prompts that cut token costs without losing output quality
  • Manage the context window using summarization, truncation, and sliding windows
  • Slash costs at scale with caching, batching, and smart model routing

Course content

5 sections • 6 lectures • 1h 24m total length

  • What Are Tokens and Why They Cost You Money8:16

    Learn how to set up N810 locally using Docker and Ngrok to receive external webhooks, using containers and secure tunnels to test automated workflows.

  • How Tokenization Works — Words, Subwords & Tokenizers9:37

Requirements

  • Basic familiarity with using an LLM API (OpenAI, Claude, or Gemini) is helpful but not required
  • An API key from any major LLM provider to follow along with the hands-on examples

Description

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.

Who this course is for:

  • Developers and builders shipping AI features who want to lower their LLM API bills
  • Founders and indie hackers running AI products that need to stay cost-sustainable at scale
  • Automation builders using tools like n8n or Make who want efficient, cheaper AI steps
  • Anyone comfortable prompting who wants to go from "it works" to "it works efficiently"

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