Description

Module 1: Introduction to Prompt Engineering (Week 1, 4 hours)

Objective: Build a foundational understanding of prompt engineering and its role in AI.
Topics:

• What is prompt engineering? Why it matters.

• Overview of large language models (LLMs) and their capabilities.

• Anatomy of a prompt: Structure, components, and intent.

• Common challenges in interacting with AI models.

Activities:

• Lecture: Introduction to LLMs and prompt engineering (1.5 hours).

• Demo: Interacting with an AI model (e.g., Grok or ChatGPT) using basic prompts (0.5 hours).

• Exercise: Craft 3 simple prompts and analyze model responses (1 hour).

• Quiz: Key concepts and terminology (0.5 hours).

Learning Outcomes:

• Explain the role of prompt engineering in AI applications.

• Identify the components of an effective prompt.

Module 2: Crafting Effective Prompts (Week 2, 4 hours)

Objective: Learn to design clear and task-specific prompts.

Topics:

• Types of prompts: Zero-shot, one-shot, few-shot, and instructional prompts.

• Best practices for prompt clarity, specificity, and context.

• Handling ambiguity and bias in prompts.

• Use cases: Text generation, question answering, summarization.

Activities:

• Lecture: Prompt types and design principles (1.5 hours).

• Hands-on Lab: Write prompts for 3 use cases (e.g., summarizing text, answering questions) and compare outputs (1.5 hours).

• Group Discussion: Share and critique prompt designs (0.5 hours).

• Assignment: Create 5 prompts for different tasks (0.5 hours, graded).

Learning Outcomes:

• Design prompts tailored to specific tasks.

• Apply best practices to minimize ambiguity in prompts.

Module 3: Advanced Prompt Engineering Techniques (Week 3, 4 hours)

Objective: Master advanced techniques to enhance model performance.
Topics:

• Chain-of-thought (CoT) prompting for reasoning tasks.

• Few-shot learning and example-based prompting.

• Prompt chaining and multi-step workflows.

• Temperature and top-k/top-p sampling for controlling model output.

Activities:

• Lecture: Advanced techniques and their applications (1.5 hours).

• Lab: Implement CoT for a math problem and few-shot for classification (1.5 hours).

• Case Study: Analyze a real-world prompt engineering example (0.5 hours).

• Quiz: Advanced techniques (0.5 hours).

Learning Outcomes:

• Apply CoT and few-shot prompting to complex tasks.

• Use prompt chaining for multi-step AI workflows.

Module 4: Evaluating and Optimizing Prompts (Week 4, 4 hours)

Objective: Learn to assess and refine prompts for optimal results.
Topics:

• Metrics for evaluating prompt performance (e.g., accuracy, relevance, coherence).

• Iterative prompt refinement process.

• Debugging common issues (e.g., hallucination, off-topic responses).

• A/B testing for prompt optimization.

Activities:

• Lecture: Evaluation metrics and optimization strategies (1.5 hours).

• Lab: Evaluate and refine 3 prompts based on model outputs (1.5 hours).

• Exercise: Conduct A/B testing with 2 prompt variations (0.5 hours).

• Assignment: Submit a refined prompt with evaluation report (0.5 hours, graded).

Learning Outcomes:

• Evaluate prompt effectiveness using quantitative and qualitative metrics.

• Iteratively improve prompts to achieve desired outcomes.

Module 5: Prompt Engineering for Real-World Applications (Week 5, 4 hours)

Objective: Apply prompt engineering to practical use cases.
Topics:

• Industry applications: Customer service, content creation, data analysis, code generation.

• Integrating prompts into applications via APIs (e.g., OpenAI, xAI).

• Ethical considerations: Bias, fairness, and responsible AI use.

• Scaling prompt engineering for production environments.

Activities:

• Lecture: Real-world applications and ethics (1.5 hours).

• Lab: Build a simple application using an AI API with custom prompts (1.5 hours).

• Case Study: Prompt engineering in a business context (0.5 hours).

• Discussion: Ethical challenges in prompt design (0.5 hours).

Learning Outcomes:

• Develop prompts for industry-specific use cases.

• Integrate prompt engineering into applications via APIs.

• Address ethical considerations in prompt design.

Module 6: Capstone Project & Course Wrap-Up (Week 6, 4 hours)

Objective: Synthesize skills through a capstone project and prepare for real-world prompt engineering.
Topics:

• • Review of key concepts and techniques.

  • Best practices for continuous learning in prompt engineering.

  • Career opportunities in AI and prompt engineering.

Activities:

• • Lecture: Course recap and career insights (1 hour).

  • Capstone Project: Design and implement a prompt-based solution for a chosen use case (e.g., chatbot, data analyzer) (2 hours).

  • Presentation: Students present projects to peers and instructor (0.5 hours).

  • Course Feedback & Certification (0.5 hours).

Learning Outcomes:

• • Apply prompt engineering skills to a complete project.

  • Present a prompt-based solution with clear documentation.

  • Identify career paths in AI and prompt engineering.