本课程深入剖析LLM分词器机制，教授提示词压缩、上下文管理、智能分块、RAG优化与模型缓存等核心技术，帮助开发者在不损失准确度的前提下，大幅降低API成本并提升响应速度。
原始标题：LLM Token Optimization: Optimize Cost, Speed, & Performance
本课程是一门聚焦于大语言模型（LLM）效能与成本控制的高阶 Token 优化实战课。核心围绕 “控本增效” 展开，指导学员深度剖析分词器（Tokenization）底层机制，通过掌握提示词压缩、上下文精细化管理、智能分块（Chunking）、高效检索（RAG 优化）与模型缓存（Caching）等核心技术，建立生产级的 Token 预算与监控体系，旨在帮助 AI 工程师和开发者在不牺牲模型输出准确度的前提下，大幅降低 API 运营成本并显著提升响应速度。
Published 7/2026
Created by Meta Brains, Shah Nawaz
MP4 | Video: h264, 1920×1080 | Audio: AAC, 44.1 KHz, 2 Ch
Level: All Levels | Genre: eLearning | Language: English | Duration: 34 Lectures ( 3h 29m ) | Size: 1.6 GB
Master Token Optimization, Prompt Engineering, Context Management, Token Budgeting, Cost Reduction, and LLM Performance
What you’ll learn
⚡ Understand how tokenization works in Large Language Models and why token optimization improves AI performance and reduces costs.
⚡ Apply prompt engineering, context management, and compression techniques to minimize token usage without sacrificing accuracy.
⚡ Optimize LLM applications using chunking, summarization, caching, retrieval strategies, and efficient prompt design.
⚡ Build scalable, cost-effective AI systems with token budgeting, performance monitoring, and production-ready optimization techniques.
Requirements
❗ Basic knowledge of AI or Large Language Models (LLMs) is helpful but not required. All token optimization concepts are explained with practical examples.
Description
Disclaimer :This course contains the use of artificial intelligence. Large Language Models (LLMs) have transformed the way AI applications are built, but every prompt, response, and interaction consumes tokens that directly affect cost, speed, and overall performance. Understanding how to optimize token usage is an essential skill for anyone building production-ready AI systems.
In this comprehensive course, you’ll learn the principles and best practices behind LLM Token Optimization. Starting with the fundamentals of tokenization, you’ll discover how tokens are generated, counted, and processed by modern language models, and why efficient token management is critical for scalable AI applications.
Throughout the course, you’ll explore prompt engineering techniques, context management, prompt compression, token budgeting, chunking strategies, summarization methods, retrieval optimization, caching, context window utilization, and efficient conversation design. You’ll also learn how to reduce unnecessary token consumption while maintaining high-quality responses and improving overall system performance.
Rather than focusing only on theory, this course emphasizes practical strategies that can be applied to real-world AI products. You’ll understand how developers optimize chatbots, AI assistants, enterprise applications, customer support systems, document processing solutions, and Retrieval-Augmented Generation (RAG) pipelines to lower operational costs and improve user experience.
By the end of this course, you’ll have the knowledge to design faster, more efficient, and cost-effective LLM applications by applying modern token optimization techniques and performance best practices.
Whether you’re an AI engineer, Python developer, prompt engineer, machine learning practitioner, product developer, or Generative AI enthusiast, this course will give you the skills needed to maximize the efficiency, scalability, and reliability of today’s most advanced AI systems.
Who this course is for
⭐ AI engineers, Python developers, prompt engineers, machine learning practitioners, GenAI enthusiasts, product developers, and anyone building cost-efficient, high-performance LLM applications.
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