Advanced Prompt Engineering
Course description
This course is dedicated to advanced methods in Prompt Engineering for large language models (LLMs) and their effective application in various scenarios. Students will study cutting-edge techniques such as prompt chaining, PAL, and ReAct, and learn to apply them to real-world tasks.
Upon completing the course, students will be able to skillfully design complex prompts and build intelligent AI systems using advanced approaches for interacting with LLMs.
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Course Requirements
- If you are a beginner in prompt engineering, it is recommended to first take the course "Introduction to Prompt Engineering".
- The main tool for the course is Flowise AI (a no-code platform for building complex AI processes).
- Some modules will require basic use of Python.
- A paid OpenAI account is required for working with the API (setup instructions are provided in the course).
Course Topics
During the course, students will master Flowise AI and learn and apply advanced prompt writing techniques.
Main topics of the course:
1. Introduction to Flowise AI
- Introduction to a popular no-code tool for creating advanced chat flows with LLMs.
- Setup of the working environment and API keys.
2. Prompt Chaining
- How to combine multiple prompts in sequence to perform complex tasks.
- Examples and practical exercises to master the technique.
3. Creating a Chatbot with LLMs
- Development of a food recommendation chat using prompt chaining.
- Step-by-step practical guide to creating a functional chatbot in Flowise AI.
4. PAL (Program-Aided Language Models)
- Use of programmatic thinking in AI.
- How LLMs can generate and execute code to solve complex tasks.
- Live demonstration of PAL capabilities and its practical application.
5. ReAct Prompting (Reasoning + Acting)
- Overview of the ReAct technique, which combines logical reasoning and action planning in LLMs.
- Practical exercises on creating AI capable of making decisions based on reasoning.
6. Developing an Advanced Agentic Chatbot (Agentic Food Chatbot)
- Final course project: development of an AI bot for nutrition recommendations.
- Use of prompt chaining, ReAct, and agentic components in Flowise AI.
- Step-by-step guide to creating an intelligent, autonomous LLM bot.
Who this Course is For
The course will be beneficial for professionals in the fields of artificial intelligence, chatbot development, process automation, customer support, marketing, data analysis, and anyone who wants to master advanced interaction methods with LLMs.
Conclusion
After completing the course, students will be able to effectively design prompts for complex AI systems, develop intelligent chatbots, and apply advanced prompt engineering techniques to optimize interaction with LLMs.
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All Course Lessons (15)
| # | Lesson Title | Duration | Access |
|---|---|---|---|
| 1 | Introduction Demo | 00:27 | |
| 2 | Course Objectives and Structure | 01:31 | |
| 3 | Installing Flowise AI | 02:50 | |
| 4 | Basic Chatflow with Flowise AI | 05:47 | |
| 5 | Introduction to Prompt Chaining | 02:22 | |
| 6 | Applied Prompt Chaining | 14:04 | |
| 7 | Food Chatbot with Prompt Chaining | 02:30 | |
| 8 | Building the Food Chatbot | 11:52 | |
| 9 | Introduction to PAL | 03:00 | |
| 10 | PAL Demo | 06:02 | |
| 11 | Introduction to ReAct Prompting | 05:26 | |
| 12 | ReAct Demo | 08:40 | |
| 13 | ReAct Under the Hood | 07:18 | |
| 14 | Agentic Food Chatbot | 01:16 | |
| 15 | Agentic Food Chatbot with Flowise AI | 10:52 |
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