CF

Introduction to Prompt Engineering

1h 27m 29s
English
Paid

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.

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 (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 to Prompt Engineering
All Course Lessons (38)
#Lesson TitleDurationAccess
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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Frequently asked questions

What prerequisites are needed before enrolling in this course?
There are no formal prerequisites for this course. However, a basic understanding of language models and general programming concepts would be beneficial. The course covers foundational topics such as 'What are LLMs?' and 'Base LLM vs. Instruction-Tuned LLM,' which can help bridge any initial knowledge gaps.
What practical projects will I work on during the course?
The course includes practical exercises such as creating a 'Movie recommendations with CoT' and a 'Food Chatbot with CoT.' These projects allow students to apply chain-of-thought prompting techniques to real-world scenarios, demonstrating the practical application of the concepts taught throughout the lessons.
Who is the target audience for this course?
This course is designed for individuals interested in leveraging large language models (LLMs) across various fields. Whether you are a data scientist, software developer, or simply someone curious about AI, the course provides foundational skills in prompt engineering necessary for effective interaction with LLMs.
How does this course compare to other courses on LLMs?
Unlike general courses on language models, this course specifically focuses on prompt engineering, a crucial skill for optimizing LLM performance. It covers a wide range of techniques such as 'Few-shot prompting,' 'Zero-shot prompting,' and 'Chain-of-thought Prompting,' providing a detailed approach to crafting effective prompts tailored to different tasks.
What platforms or tools will I use in this course?
The course involves working with the OpenAI Playground, as detailed in lessons like 'Introduction to the OpenAI Playground' and 'OpenAI Playground - Roles.' This platform is essential for experimenting with different prompting techniques and understanding how changes in parameters, such as 'Temperature,' affect LLM outputs.
What topics are not covered in this course?
The course does not cover the internal architecture or training processes of LLMs. It focuses on prompt engineering techniques rather than the technical details of how LLMs are built or trained. Those interested in model development or deep learning frameworks will need to seek additional resources beyond this course.
How much time should I expect to commit to this course?
The course consists of 38 lessons, but the total runtime is unspecified. Given the comprehensive nature of the topics, including practical exercises like 'Movie recommendations with CoT,' learners should expect to dedicate several hours per week to complete the exercises and absorb the material effectively.