LLM Engineering: Prompting, Fine-Tuning, Optimization & RAG

This course provides a comprehensive, hands-on journey into model adaptation, fine-tuning, and context engineering for large language models (LLMs). It focuses on how pretrained models can be efficiently customized, optimized, and deployed to solve real-world NLP problems across diverse domains. Through structured lessons, demonstrations, and practice assignments, you will learn how to apply transfer learning, parameter-efficient fine-tuning techniques, context engineering strategies, and optimization methods to build scalable and production-ready LLM systems. The course emphasizes both theoretical foundations and practical workflows using modern tooling such as Hugging Face, Trainer APIs, and model monitoring platforms. By the end of this course, you will be able to: - Explain the principles of transfer learning, model adaptation, and parameter-efficient fine-tuning for large language models - Fine-tune pretrained models using techniques such as LoRA and adapters for domain-specific and task-based applications - Design effective context engineering strategies, including context optimization, compression, and scalable context patterns - Evaluate fine-tuned models using task-appropriate metrics and perform error analysis - Optimize, deploy, monitor, and maintain fine-tuned models for efficient and cost-effective production use This course is ideal for machine learning engineers, AI practitioners, NLP developers, and data scientists who want to move beyond prompt-only interactions and gain practical expertise in adapting and deploying LLMs in real-world systems. A working knowledge of Python, machine learning fundamentals, and basic NLP concepts is recommended to get the most out of this course. Join us to master the end-to-end lifecycle of fine-tuning, optimizing, and operationalizing large language models—from pretrained foundations to scalable, production-ready AI solutions.

This course offers a comprehensive, hands-on exploration of prompt engineering as a core skill for working effectively with large language models (LLMs). It focuses on how prompts can be deliberately designed, structured, evaluated, and scaled to guide model behavior, improve reasoning quality, and build reliable AI-driven applications—without modifying model weights. Through a progression of foundational concepts, advanced strategies, and real-world demonstrations, you will learn how to craft high-quality prompts, apply proven prompt patterns such as few-shot and chain-of-thought prompting, manage context and memory, and systematically evaluate and refine prompt performance. The course emphasizes practical workflows using modern tooling such as LangChain, prompt templates, evaluation frameworks, and automation techniques. By the end of this course, you will be able to: - Explain the principles and objectives of prompt engineering and its role in controlling LLM behavior - Design effective prompt structures using techniques such as few-shot prompting, chain-of-thought reasoning, and role-based prompts - Manage long context and conversational memory to build coherent, multi-turn LLM interactions - Evaluate, test, and refine prompts using qualitative metrics, automated feedback, and ranking methods - Build reusable, scalable prompt systems that support multimodal inputs, domain-specific use cases, and production workflows This course is ideal for software developers, machine learning engineers, AI practitioners, prompt designers, and data scientists who want to move beyond ad-hoc prompting and develop systematic, testable, and reusable prompt-driven solutions for LLM applications. A basic understanding of Python, familiarity with LLM concepts, and experience interacting with generative AI models are recommended to get the most value from this course. Join us to master the art and engineering of prompts—from simple instructions to robust, reusable prompt systems that power reliable and scalable LLM-based applications.