This comprehensive Prompt Engineering course equips you with the skills to design, optimize, and scale effective prompts for generative AI and large language models. Begin by mastering the structure of prompts, learn how to use key elements like instructions, context, input data, and output indicators to generate precise outputs. Explore LLM settings and formatting techniques to enhance prompt effectiveness. Progress to core techniques such as zero-shot, few-shot, Chain of Thought (CoT), Self-Consistency, and Tree of Thoughts (ToT) prompting, reinforced with practical demos using OpenAI and LangChain. Learn to generate synthetic data for RAG models and create dynamic, reusable prompts using LangChain templates, Jinja2, and Python f-strings.

You should have a basic understanding of Python programming and familiarity with large language model outputs. By the end of this course, you will be able to: - Understand Prompts: Master structure and elements for accurate AI outputs - Apply Techniques: Use zero-shot, few-shot, CoT, and advanced strategies - Build Dynamically: Create reusable prompts with LangChain and templates - Scale with GenAI: Design prompt-driven workflows for real-world use cases Ideal for AI developers, data scientists, and professionals building GenAI-powered applications.