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AI - Prompt Engineering Techniques
18 students
What you'll learn
- Understand the Fundamentals of Prompt Engineering
- Implement Zero-Shot and Few-Shot Learning Techniques
- Master Multi-Step Reasoning with Chain of Thought (CoT) Prompting
- Apply In-Context Learning Approaches
- Design Custom Workflows Using Python and LangChain
- Evaluate and Optimize Prompts for Different Use Cases
- Leverage Advanced Prompting Techniques
Explore related topics
Course content
2 sections • 22 lectures • 2h 38m total length
Requirements
- No programming experience is needed
- Willing to learn more...
Description
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 COURSE
1. 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 reasoning.
Comparing CoT performance against standard prompts.
Advanced CoT techniques for multi-step problem-solving.
5. Python and LangChain Integration
Introduction to the LangChain library for prompt engineering workflows.
Building interactive applications with LangChain and OpenAI models.
Automating and scaling prompt-based tasks in Python.
6. Evaluation and Optimization
How to test and refine prompts for accuracy and relevance.
Performance evaluation: Comparing results across use cases.
Tips for optimizing prompts for specific industries or challenges.
7. Hands-On Projects
Design AI workflows for real-world problems (content creation, coding assistants, customer support, etc.).
Build and deploy an AI-powered application using LangChain and Python.
Are you ready to become a master in prompt engineering?
This is more than just a course—it’s your gateway to building intelligent, impactful AI applications. Gain practical skills, learn industry-leading techniques, and join a growing community of AI innovators.
Don’t wait—enroll now and start shaping the future with AI!
Click Join Now to begin your journey to AI Prompt Engineering mastery!
Who this course is for:
- Want to master on "How to talk" to large language models
- Individuals interested in understanding and leveraging the power of AI and OpenAI's GPT models.
- Professionals who want to enhance their AI workflows by mastering few-shot learning, in-context learning, and multi-step reasoning.
- Creatives who want to utilize AI for generating content, ideas, or solutions in innovative ways.
- Developers looking to integrate prompt engineering techniques into their Python projects and applications.