This course is dedicated to the key methods of Prompt Engineering for large language models (LLMs) and their effective application in various scenarios and tasks. Upon completion of the course, students will obtain a clear and systematic methodology for creating effective prompts, enabling the potential of LLMs to be unlocked in different fields.
Introduction to Prompt Engineering
Course Requirements
- No prior knowledge is required.
- The main tool will be OpenAI Playground, so programming is not required.
- A paid OpenAI account is needed (registration and setup instructions are provided in the course).
Course Topics
During the course, students will use OpenAI Playground to develop and optimize prompts in various scenarios.
Main topics of the course:
Introduction to LLMs
Basics of Large Language Models (LLMs): their types, applications, and usage strategies. The course covers both basic concepts and practical applications, helping to effectively use LLMs in real-world tasks.
Fundamentals of Prompt Engineering
How to design effective prompts correctly? Why is this important? We will examine key principles of writing prompts and learn to formulate initial requests for optimal interaction with LLMs.
OpenAI Playground
Learning the interface of OpenAI Playground and managing model behavior. Practical exercises include:
- Assigning roles,
- Setting temperature,
- Role modeling,
- Text classification.
Improving Prompts
We will analyze key elements of effective prompts:
- Clarity of formulations,
- Use of delimiters,
- Control of response length,
- Output formatting.
Few-shot prompting
We will master the technique of few-shot prompting to improve LLM performance with examples. You will learn:
- How to choose examples for prompts correctly,
- The optimal number of examples,
- How to format them to achieve the best results.
Information Extraction (Use Case: Information Extraction)
Practical use of prompt engineering for extracting structured information from text. We will consider zero-shot and few-shot approaches for quick and accurate data extraction from various types of content.
Chain-of-Thought Prompting
The method of logical response construction (Chain-of-Thought prompting) allows LLM to perform complex reasoning. Practical exercise: creating a movie recommendation system. Upon completion - a comprehension test.
Chatbot Development (Use Case: Chatbot)
The final project of the course: creation and optimization of a chatbot prompt using all the learned techniques and best practices.
After completing the course, you will be able to develop prompts for LLMs, optimize interaction with AI, and use models in business, analytics, marketing, research, and chatbot development.
About the Author: DAIR.AI (Elvis Saravia)
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.
Watch Online 38 lessons
| # | Lesson Title | Duration | Access |
|---|---|---|---|
| 1 | Introduction to Prompt Engineering Demo | 00:29 | |
| 2 | About the Instructor | 00:42 | |
| 3 | Course Objectives | 00:35 | |
| 4 | Course Structure | 00:56 | |
| 5 | The tools and environment | 00:48 | |
| 6 | Setting up your Playground | 01:46 | |
| 7 | What are LLMs? | 00:53 | |
| 8 | Base LLM vs. Instruction-Tuned LLM | 02:09 | |
| 9 | LLMs and LLM Providers | 00:41 | |
| 10 | Chat LLMs | 01:05 | |
| 11 | Chat LLM Common Use Cases | 00:57 | |
| 12 | How to Leverage LLMs? | 00:27 | |
| 13 | What is Prompt Engineering? | 01:03 | |
| 14 | Why Prompt Engineering? | 01:07 | |
| 15 | Elements of a Prompt | 02:23 | |
| 16 | First Basic Prompt | 02:24 | |
| 17 | Introduction to the OpenAI Playground | 01:41 | |
| 18 | OpenAI Playground - Roles | 04:25 | |
| 19 | OpenAI Playground - Temperature | 04:00 | |
| 20 | OpenAI Playground - Text Classification | 04:28 | |
| 21 | OpenAI Playground - Role Playing | 03:46 | |
| 22 | What makes a good prompt? | 02:32 | |
| 23 | Be clear and specific when prompting | 01:19 | |
| 24 | Using delimiters | 03:30 | |
| 25 | Specifying output length | 02:27 | |
| 26 | Output format | 01:23 | |
| 27 | Split Complex Tasks into Subtasks | 02:44 | |
| 28 | Introduction to Few-shot prompting | 02:16 | |
| 29 | How many demonstrations? | 02:01 | |
| 30 | Tips for preparing demonstrations | 01:57 | |
| 31 | Extracting information | 02:17 | |
| 32 | Zero-shot prompting | 03:24 | |
| 33 | Few-shot prompting | 06:31 | |
| 34 | Chain-of-thought Prompting | 03:19 | |
| 35 | Movie recommendations with CoT | 05:12 | |
| 36 | Food Chatbot with CoT | 05:55 | |
| 37 | Recap of the course | 01:40 | |
| 38 | Future of Prompt Engineering | 02:17 |
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