Building and Fine-Tuning LLM Applications

Integrating large language models into software applications requires a solid grasp of both foundational concepts and modern engineering patterns. This text-based course guides you from the basics of generative AI to deploying your own customized language model workflows. You will transition from understanding how language models work to actively building and adapting them for specific use cases. Through clear written explanations, practical code walk-throughs, and structured exercises, you will gain the confidence to design robust AI-driven applications. What you'll learn: 1. Understand foundational LLM architecture, tokenization, and key generative AI terminology. 2. Design effective prompts using systematic prompt engineering patterns. 3. Implement Retrieval-Augmented Generation (RAG) to connect LLMs with external vector databases. 4. Explore parameter-efficient fine-tuning methods like LoRA to adapt models to custom datasets. 5. Evaluate model performance and address common issues like hallucination. 6. Structure LLM application workflows using modern orchestration frameworks. The curriculum begins with essential definitions and foundational architectures before moving into practical application design. You will explore hands-on implementation strategies, progressing from simple API integrations to advanced fine-tuning and evaluation methodologies. This course is designed for software developers, data enthusiasts, and tech professionals who are new to LLM development. No prior machine learning experience is required, though basic Python knowledge is helpful. Start reading today to build and customize your first intelligent language model application.