Introduction to Prompt Engineering

1h 27m 29s
English
Paid

Course description

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.

Read more about the course

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.

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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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