Getting useful output from a large language model is less about luck and more about method. This course builds a systematic approach to writing prompts, worked through directly inside the OpenAI Playground rather than through abstract theory.
What you'll work through
- The basics of large language models: their types, applications, and general usage strategies
- Core principles behind designing an effective prompt, and why they matter
- Hands-on practice in the OpenAI Playground, including assigning roles, adjusting temperature, and role modeling
No programming background is required, though you'll need a paid OpenAI account to follow along, with setup instructions provided as part of 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.
