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.

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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# Title Duration
1 Introduction to Prompt Engineering 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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