LLM Evaluation and Guardrails for Production

Building a prototype with large language models is easy, but making it safe, reliable, and ready for real-world users requires a structured approach. This text-based course guides you through the essential methodologies for testing, securing, and monitoring your LLM applications. You will transition from basic prompting to deploying robust applications with confidence. By understanding how to measure output quality, prevent prompt injections, and set up continuous monitoring, you will ensure your AI systems remain safe, accurate, and cost-effective. What you'll learn: Learn the fundamental concepts of LLM evaluation, including automated metrics and human-in-the-loop testing workflows; Implement safety guardrails to detect and block toxic content, hallucinations, and prompt injection attacks; Understand retrieval-augmented generation (RAG) evaluation patterns to measure retrieval accuracy and response generation; Apply modern observability concepts to track latency, token usage, and semantic drift in production environments; Configure system-level checks to maintain compliance, data privacy, and cost-efficiency. The course starts with foundational definitions and key terminology before progressing to practical evaluation strategies and active guardrail implementation. You will explore real-world scenarios through clear written explanations and structured code snippets. This course is designed for developers, product managers, and technical beginners who are new to LLM operations, with no advanced machine learning background required. Start reading today to build secure and dependable AI applications that your users can trust.