Optimizing LLM Costs and Streamlining AI Workflows

Deploying Large Language Models (LLMs) can quickly become expensive and inefficient without the right strategy. This text-based course teaches you how to balance high performance with budget-friendly practices. You will transition from basic API integration to building highly optimized, cost-conscious AI workflows using modern industry techniques. What you'll learn: Learn foundational LLM concepts, pricing models, and token math; Implement prompt engineering strategies to minimize token usage without losing quality; Apply caching mechanisms to reuse previous outputs and reduce API calls; Configure model routing and smaller open-source models for simpler tasks; Explore Retrieval-Augmented Generation (RAG) architectures to limit context window bloat; Design efficient pipeline workflows that streamline data processing. The course begins with essential terminology and cost metrics before guiding you through practical optimization techniques and modern architectural patterns. This course is designed for beginners, developers, and tech-curious professionals looking to build sustainable AI applications; no prior machine learning experience is required. Start reading today to build smarter, faster, and more affordable AI solutions.