Title: Course Objectives
Course: LLM_Prompt_Engineering_For_Developers > Section: Module 68: Course Objectives

Course Objectives

Course Objectives for “LLM Prompt Engineering for Developers”

1. Understanding LLMs
  - Objective: By the end of this section, students will be able to explain the fundamental principles of Large Language Models (LLMs) and identify at least three popular LLMs, including their key features and functionalities, with an emphasis on GPT-3, GPT-4, and BERT.
2. Basics of Prompting
  - Objective: Students will be able to define what a prompt is and categorize at least five different types of prompts (e.g., question-based, statement-based) relevant to LLMs, demonstrating an understanding of how each type affects model output.
3. Crafting Effective Prompts
  - Objective: Students will create a minimum of three original, clear, and specific prompts for given tasks, employing techniques to minimize ambiguity, and evaluate the effectiveness of these prompts in generating desired responses from LLMs.
4. Iterative Prompt Development
  - Objective: By the end of this section, students will demonstrate the ability to apply an iterative approach by testing, refining, and improving at least two prompts based on feedback loops, resulting in measurable enhancements in prompt effectiveness.
5. Handling LLM Responses
  - Objective: Students will analyze and interpret responses from LLMs for two different prompts, identifying at least three common types of unexpected or incorrect outputs, and will develop strategies for mitigating these issues in practical scenarios.
6. Use Cases and Applications
  - Objective: After completing the course, students will be able to identify and describe at least three practical applications of prompt engineering in real-world contexts (e.g., chatbots, content generation) and will present a case study showcasing a successful implementation, highlighting the role of effective prompting.
7. Ethical Considerations in Prompt Engineering
  - Objective: Students will outline the ethical implications of prompt engineering, discussing bias in LLMs and how prompts can influence outcomes, and will propose at least two responsible practices for using LLMs in development projects to ensure ethical adherence.

These objectives serve as a framework guiding students through the critical aspects of prompt engineering, fostering both theoretical knowledge and practical skills essential for working with LLMs.

Course Outcomes