LLM Bootcamp

Large language models are now a core part of many modern software systems, including coding assistants, semantic search, and enterprise automation workflows. As teams integrate AI into production systems, the main challenge is designing, adapting, and evaluating models that fit the application. In this LLM bootcamp, you’ll move beyond isolated experiments toward production-ready LLM applications, where choices about prompting, fine-tuning, and retrieval affect latency, output quality, reliability, and cost. I created this course from my work in adaptive AI, neural networks, and intelligent systems, along with years of research in intelligent computing and tutoring systems. Across both academic and applied environments, I’ve seen a consistent gap: developers understand LLM capabilities at a surface level but struggle to translate them into scalable, production-ready solutions. This bootcamp reflects a structured approach I’ve refined to bridge that gap, focusing on practical decision-making rather than just theory. The course begins with the core mechanics of LLMs, including transformers, tokenization, and embeddings, and builds toward advanced topics like prompt engineering, security risks, and token optimization. You’ll explore when to fine-tune a model and when to use RAG, apply techniques such as LoRA, and understand where RLHF fits in model alignment workflows. You’ll also design agentic workflows in which LLMs use tools, memory, and control flow to complete multi-step tasks, leveraging LangChain and LangGraph. You’ll apply each concept to practical LLM application patterns, building toward end-to-end pipelines and evaluation strategies used in production LLM systems. Developers and AI practitioners are already using this approach to build reliable, scalable LLM applications across industries. If your goal is to move from understanding LLMs to confidently building with them, this bootcamp will give you that foundation.