Prompt Engineering: Optimizing AI for Cost and Quality

Running large language models in production can quickly become expensive if your prompts are inefficient. This text-based course helps you balance the trade-offs between API costs and response quality. You will learn to write structured, token-efficient prompts that deliver precise results without wasting resources. By understanding how models process text, you will be able to optimize your AI integrations for speed, accuracy, and budget. What you'll learn: Understand the core concepts of prompt engineering, tokenization, and API pricing models; Apply token-saving techniques to reduce prompt length without losing context; Implement structured outputs like JSON to ensure reliable and parsing-friendly responses; Analyze the trade-offs between different model sizes and routing strategies for cost-efficiency; Design evaluation frameworks to measure prompt quality and latency systematically; Practice optimization strategies through written examples and guided code snippets. The course begins with foundational definitions of tokens and model architecture before moving into practical prompt design, caching strategies, and cost-reduction workflows. You will progress from basic prompt construction to advanced budget-management techniques through clear explanations and structured exercises. This course is designed for beginners, developers, and product minds looking to build cost-effective AI solutions, with no prior prompt optimization experience required. Start reading today to build smarter, faster, and more budget-friendly AI applications.