Advanced Prompt Engineering Course

Overview

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.

Syllabus

Foundations of Prompt Engineering

Master the foundations of prompt engineering with this hands-on module. Learn how to craft effective prompts, understand key elements like instructions, context, input data, and output indicators. Explore advanced techniques including LLM settings and prompt formatting for optimal results. Ideal for professionals looking to harness the power of generative AI tools efficiently.

Core Prompting Techniques

Explore core prompting techniques to maximize the performance of large language models. Learn zero-shot, few-shot, and Chain of Thought (CoT) prompting to improve response accuracy and reasoning. Dive into advanced strategies like Self-Consistency and Tree of Thoughts (ToT) prompting with real-world demos using OpenAI and LangChain. Perfect for anyone mastering GenAI workflows.

Applications and Tools for Prompt Engineering

Discover real-world applications and tools for effective prompt engineering. Learn how to generate synthetic data for RAG models and create powerful prompts using LangChain. Explore prompt templates, chat prompts, and dynamic message generation using Jinja2 and Python f-strings. This module is ideal for developers building GenAI-powered applications and custom LLM workflows.