AI - Prompt Engineering Techniques

About this course

Are you ready to revolutionize the way you interact with AI? This course, Prompt Engineering Using Python, is your ultimate guide to mastering the art and science of crafting effective prompts that maximize the potential of OpenAI’s GPT models. Whether you're solving complex problems, building AI-powered applications, or enhancing workflows, this course is packed with actionable techniques and real-world examples to take your skills to the next level!

From zero-shot learning to advanced chain-of-thought (CoT) reasoning, this course dives deep into the nuances of prompt engineering. You’ll explore few-shot learning, in-context learning, and multi-step reasoning, using cutting-edge tools like Python and the LangChain library. With hands-on projects and best practices, you’ll gain the confidence to apply these techniques to real-world scenarios.

You will learn the following and more in this PRACTICAL COURSE1. Introduction to Prompt Engineering

What is prompt engineering, and why does it matter?

The principles of crafting effective prompts.

Introduction to OpenAI’s GPT models and their capabilities.2. Zero-Shot and Few-Shot Learning

Overview of zero-shot and few-shot learning techniques.

Practical implementation in Python using real-world examples.

Best practices for example selection in few-shot learning.3. In-Context Learning

Understanding in-context learning and its applications.

Designing prompts with contextual examples to improve model responses.

Real-world scenarios for in-context learning.4. Chain of Thought (CoT) Prompting

Breaking down complex problems with CoT reasoni

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