Udemy – AI – Prompt Engineering Techniques

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.