CF

Advanced Prompt Engineering

1h 23m 57s
English
Paid

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.

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.

About the Author: DAIR.AI (Elvis Saravia)

DAIR.AI (Elvis Saravia) thumbnail

DAIR.AI (Democratizing Artificial Intelligence Research) is the educational arm founded by Elvis Saravia, a former Meta AI researcher and the maintainer of one of the most-starred prompt-engineering reference repositories on GitHub. The brand has become one of the more authoritative independent sources on the practical engineering side of LLM applications.

The CourseFlix listing carries five DAIR.AI courses spanning the applied AI track: Introduction to Prompt Engineering, Advanced Prompt Engineering, Introduction to RAG, Introduction to AI Agents, and Cursor — Coding with AI.

Material is paid and aimed at engineers picking up applied LLM and AI-coding work as deliberate professional skills. For broader content, see CourseFlix's Prompt Engineering, RAG, AI Agents, and AI-Assisted Coding category pages.

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#1: Introduction
All Course Lessons (15)
#Lesson TitleDurationAccess
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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Frequently asked questions

What prerequisites are required for this course?
The course is designed for individuals who have a foundational understanding of machine learning and artificial intelligence concepts. Familiarity with large language models, as well as some experience in programming, particularly in Python, will be beneficial. Students should also be comfortable using AI tools and platforms, as the course involves practical exercises with Flowise AI and other prompt engineering techniques.
What projects will I work on during the course?
Students will engage in several hands-on projects throughout the course, including building a Food Chatbot using prompt chaining techniques. Additionally, students will develop agentic food chatbots utilizing Flowise AI, showcasing their ability to apply advanced prompt engineering methods like PAL and ReAct to real-world applications.
Who is the target audience for this course?
This course is intended for AI professionals, data scientists, and developers who are looking to enhance their skills in prompt engineering for large language models. It's ideal for those who want to explore advanced techniques such as prompt chaining, PAL, and ReAct, and apply these methods to create intelligent AI systems.
How does this course compare to other LLM courses in terms of depth and scope?
This course provides a focused exploration of advanced prompt engineering techniques, diving into specific methods like prompt chaining, PAL, and ReAct. Unlike more general LLM courses, it emphasizes practical application through projects like building chatbots with Flowise AI, offering a deeper dive into cutting-edge methodologies for interacting with large language models.
Which tools and platforms will I use in this course?
Students will primarily use Flowise AI to build and experiment with chatbots and other applications involving large language models. The course covers the installation and basic use of Flowise AI, followed by more advanced applications of prompt chaining, PAL, and ReAct techniques.
Is there anything that is not covered in this course?
The course does not cover foundational AI or machine learning concepts in depth, as it assumes a basic understanding of these areas. It focuses specifically on advanced prompt engineering techniques and their applications, without delving into other areas of AI development or unrelated LLM frameworks.
What is the expected time commitment for this course?
The course consists of 15 lessons, each designed to build on the previous one. While the exact runtime is not specified, students should anticipate dedicating several hours per lesson to fully engage with the course materials, complete hands-on projects, and practice the advanced techniques introduced throughout the course.