| File Name: | Udemy – AI – Prompt Engineering Techniques |
| Content Source: | https://www.udemy.com/course/ai-prompt-engineering-techniques/ |
| Genre / Category: | Other Tutorials |
| File Size : | 1.3 GB |
| Publisher: | udemy |
| Updated and Published: | January 22,2025 |
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.
DOWNLOAD LINK: Udemy – AI – Prompt Engineering Techniques
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