Engineering Reliable LLM Systems

Many developers can write a basic prompt, but building a Large Language Model (LLM) application that works consistently in production requires a systematic engineering approach. This text-based course guides you through the process of designing, evaluating, and securing LLM-powered applications. You will transition from simple prototyping to establishing robust evaluation frameworks, managing system latency, and mitigating common failure modes like hallucinations. What you'll learn: - Understand foundational LLM concepts, architectures, and the core components of reliable system design. - Master advanced prompt engineering techniques and structured output generation for consistent results. - Implement Retrieval-Augmented Generation (RAG) patterns to connect models with external data sources securely. - Establish systematic evaluation frameworks to measure accuracy, latency, and cost across model iterations. - Configure guardrails and safety protocols to mitigate hallucinations, prompt injection, and unwanted outputs. - Monitor production LLM systems to track performance drift and user interactions in real time. The course starts with essential terminology and the mechanics of language models before moving step-by-step through prompt design, data retrieval integration, evaluation strategies, and production monitoring. It is designed for software developers, data practitioners, and tech-savvy beginners interested in AI engineering, requiring no prior machine learning background. Start reading today to build AI applications you can trust.