3 LLM Cost Optimization Tricks Every Engineer Needs

Stop wasting tokens. In this video, I’ll show you 3 AI token-efficiency hacks that instantly cut your LLM costs by up to 50% — with real examples engineers can use right now. You’ll learn how to: ✅ Compress prompts without losing meaning ✅ Batch & reuse context the right way ✅ Use model-cascading to save tokens automatically ✅ Reduce output size with structured responses ✅ Build smarter, cheaper AI workflows for engineering tasks Whether you’re using ChatGPT, Claude, Gemini, OpenAI API, Anthropic, or local LLMs, these techniques work across all models. If you build AI tools, write technical prompts, or run production workloads, this video will show you exactly how to cut cost, reduce latency, and boost performance with simple prompt engineering tricks. 📌 What this video covers: • Token-efficient prompting • LLM cost optimization strategies • AI workflow design for engineers • How to reduce token usage in real projects • Best practices for structured prompting (JSON mode) • Beginner-friendly + practical demos Free Token Optimizer tool : https://token-optimizer.devopspod.com Key moments : 0:36 Intro on LLM Model token costing 0:37 How to batch multiple tasks into one AI request 1:34 How to reuse context to cut LLM cost 2:38 How to use model cascading to save tokens 3:16 How to structure AI outputs to reduce token count