Prompt Engineering for LLMs

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.