Improving LLM Application Accuracy: Evaluation and Prompt Tuning

Building applications with Large Language Models is easy, but ensuring they generate accurate, consistent, and reliable outputs in production is a major challenge. This text-based course guides you through the essential methodologies to systematically measure and improve the quality of your AI-powered systems. You will transition from trial-and-error prompting to rigorous, data-driven optimization techniques. What you'll learn: - Understand the core concepts of LLM evaluation, accuracy metrics, and why standard software testing fails for generative AI. - Design robust evaluation datasets and establish baseline performance metrics for your applications. - Apply systematic prompt engineering techniques, including few-shot learning and chain-of-thought prompting, to boost output reliability. - Configure memory tuning and context management strategies to maintain accuracy over long conversations. - Explore retrieval-augmented generation patterns to ensure external data sources are used correctly. - Implement automated testing workflows to catch regressions and monitor LLM accuracy over time. You will start with the foundational definitions of LLM behavior and evaluation theory before moving on to practical prompting adjustments, memory management, and modern automated validation workflows. This course is designed for software developers, product managers, and tech enthusiasts who are new to LLM engineering and want to make their AI features production-ready. No prior experience in machine learning or data science is required. Start reading today to build smarter, more accurate LLM applications with confidence.