Course Overview
In this course, you will learn how to create and optimize prompts for a variety of generative AI models. First, this course covers the basics of foundation models, including a subset of foundation models (FMs), called large language models (LLMs). Then, the course covers the fundamental concepts of prompt engineering, such as the different elements of a prompt and some general best practices for using prompts effectively. Finally, the course provides information about basic prompt techniques, including zero-shot, few-shot, and chain-of-thought (CoT) prompting.
- Level: Fundamental
- Duration: 60 minutes
Moyens d'évaluation :
- Quiz pré-formation de vérification des connaissances (si applicable)
- Évaluations formatives pendant la formation, à travers les travaux pratiques réalisés sur les labs à l’issue de chaque module, QCM, mises en situation…
- Complétion par chaque participant d’un questionnaire et/ou questionnaire de positionnement en amont et à l’issue de la formation pour validation de l’acquisition des compétences
Who should attend
This event is intended for:
- Prompt engineers
- Data scientists
- Developers
Course Objectives
During this event, you will learn:
- Identify the fundamental concepts of FMs and LLMs
- Define prompt engineering and identify the best practices for designing effective prompts
- Identify the basic types of prompt techniques, including zero-shot, few-shot, and CoT techniques
Course Content
Module 1: Foundation models and large language models
- How does a foundation model function?
- Training FMs
- Types of FMs
- Large language models
- Transformer architecture
- Neural networks
- LLM use cases
Module 2: Key concepts of prompt engineering
- Fine-tuning and prompt engineering
- Elements of a prompt
- Best practices for designing effective prompts
- Practice with prompts
Module 3: Basic prompt techniques
- Zero-shot prompting
- Few-shot prompting
- Chain-of-thought prompting
Allemand
Moyens Pédagogiques :