Production-Ready LLM Applications and RAG Systems

Moving a Large Language Model from a simple prototype to a reliable production environment requires a solid understanding of architecture, data flow, and evaluation. This text-based course guides you through the foundational concepts and practical patterns needed to build robust AI applications. You will transition from understanding basic LLM prompts to designing secure, scalable Retrieval-Augmented Generation (RAG) pipelines. By reading through clear explanations and structured code snippets, you will gain the confidence to implement, monitor, and optimize language model workflows in real-world scenarios. What you'll learn: - Understand the foundational architecture of Large Language Models and how they process information. - Implement Retrieval-Augmented Generation (RAG) patterns to connect LLMs with external data sources. - Configure vector databases to store, index, and retrieve high-dimensional semantic embeddings. - Apply prompt engineering techniques to improve model accuracy and reduce hallucinations. - Evaluate LLM outputs using structured metrics and basic observability frameworks. - Deploy AI applications securely while managing latency, API costs, and rate limits. The course begins with core definitions and LLM mechanics before guiding you through vector search setup, RAG integration, and production-level monitoring strategies. You will progress systematically through conceptual readings and step-by-step code analysis. This course is designed for software developers, data enthusiasts, and tech professionals who are new to AI engineering and want to build production-grade applications. No prior experience with machine learning or AI modeling is required. Start reading today to bridge the gap between AI prototyping and production deployment.