Udemy - LLM Token Optimization - Enterprise Cost and Performance

LLM Token Optimization: Enterprise Cost & Performance
https://WebToolTip.com
Published 5/2026

MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz, 2 Ch

Language: English + subtitle | Duration: 1h 7m | Size: 1.25 GB
Optimize enterprise LLM spend through advanced token engineering, constrained decoding, and multi-tier orchestration
What you'll learn

Analyze the cost disparity between input and output tokens to optimize enterprise inference budgets and unit economics.

Implement semantic caching using vector embeddings to bypass redundant LLM generation cycles and reduce latency.

Design dynamic model routing systems to dispatch tasks to the most cost-effective inference engine based on complexity.

Apply algorithmic prompt minification to strip non-semantic tokens and maximize information density in instructions.

Leverage native constrained decoding to generate zero-bloat structured data and eliminate costly prompt-based formatting rules.

Utilize rolling summarization and cross-encoder reranking to manage context window saturation and reduce RAG overhead.

Deploy enterprise telemetry to track granular token consumption and attribute inference costs to specific product features.

Establish automated evaluation pipelines using LLM-as-a-Judge to maintain output quality during optimization cycles.
Requirements

Familiarity with Large Language Model concepts such as prompts, context windows, and RAG.

Basic understanding of vector databases and embedding-based search is recommended.