Prompt Engineering Course is designed to equip learners with the skills to craft effective, precise, and optimized prompts for interacting with AI models, particularly large language models (LLMs).
The course covers the fundamentals of prompt design, best practices, and advanced techniques to achieve desired outputs for various applications, including text generation, problem-solving, and creative tasks.
Prompt Engineering
- Duration: 6 weeks (4-6 hours per week)
- Level: Beginner to Advanced
- Prerequisites: Basic understanding of AI or familiarity with interacting with AI tools like ChatGPT or Grok.
- Delivery: Online, self-paced with optional live workshops
- Assessment: Quizzes, practical assignments, and a final project
Learning Objectives
By the end of this course, learners will be able to:
- Understand the principles of prompt engineering and its importance in AI interactions.
- Design clear and effective prompts for various tasks, including text generation, reasoning, and data analysis.
- Apply advanced techniques like chain-of-thought prompting, few-shot learning, and prompt tuning.
- Evaluate and optimize prompts to improve AI model performance and output quality.
- Use prompt engineering in real-world applications, such as content creation, automation, and problem-solving.
Course Outline
Week 1: Introduction to Prompt Engineering
Objective: Understand the basics of prompt engineering and its role in AI interactions.
Topics:
What is prompt engineering?
How AI models interpret prompts (tokenization, context windows, etc.).
Importance of clear and specific prompts.
Common use cases: Q&A, text generation, task automation.
Activities:
Experiment with basic prompts using a free AI tool (e.g., Grok or ChatGPT).
Analyze the difference between vague and specific prompts.
Assignment: Write 5 simple prompts and compare AI outputs for clarity and accuracy.
Resources: Introduction to LLMs, prompt engineering guides.
Week 2: Crafting Effective Prompts
Objective: Learn to design clear and structured prompts for consistent AI outputs.
Topics:
Anatomy of a good prompt: clarity, context, and constraints.
Techniques: specifying tone, format, and audience.
Handling ambiguity and bias in AI responses.
Prompt templates for common tasks (e.g., summarization, translation).
Activities:
Practice rewriting vague prompts to be more specific.
Create prompts for different tones (e.g., formal, casual, technical).
Assignment: Develop 3 prompt templates for a chosen use case (e.g., content creation, customer support).
Resources: Prompt design cheat sheets, examples of good vs. bad prompts.
Week 3: Advanced Prompting Techniques
Objective: Explore advanced strategies to enhance AI performance.
Topics:
Zero-shot, one-shot, and few-shot prompting.
Chain-of-thought (CoT) prompting for reasoning tasks.
Role-based prompting (e.g., “Act as a teacher”).
Handling complex tasks with multi-step prompts.
Activities:
Experiment with few-shot prompts for classification tasks.
Use CoT prompting to solve a math or logic problem.
Assignment: Create a chain-of-thought prompt to solve a complex problem (e.g., a business case study).
Resources: Research papers on CoT prompting, few-shot learning examples.
Week 4: Optimizing and Debugging Prompts
Objective: Learn to refine prompts and troubleshoot suboptimal AI outputs.
Topics:
Identifying common issues: irrelevant responses, hallucination, incomplete outputs.
Iterative prompt refinement process.
Using constraints and delimiters for structured outputs.
Evaluating prompt performance (accuracy, relevance, efficiency).
Activities:
Debug a poorly performing prompt and document improvements.
Test prompts with different constraints (e.g., word limits, JSON output).
Assignment: Optimize a given prompt to achieve a specific output format (e.g., JSON, bullet points).
Resources: Debugging checklists, prompt evaluation frameworks.
Week 5: Prompt Engineering for Specific Applications
Objective: Apply prompt engineering to real-world use cases.
Topics:
Content creation: blogs, social media posts, marketing copy.
Data analysis: summarizing datasets, generating insights.
Automation: scripting, customer service chatbots.
Creative applications: storytelling, poetry, game design.
Activities:
Create prompts for generating a blog post outline.
Design a prompt for summarizing a dataset (e.g., sales data).
Assignment: Develop a set of prompts for a chosen application (e.g., automate email responses).
Resources: Industry case studies, prompt libraries for specific domains.
Week 6: Capstone Project and Advanced Topics
Objective: Synthesize skills and explore cutting-edge prompt engineering techniques.
Topics:
Prompt tuning and fine-tuning for specific models.
Ethical considerations in prompt engineering (e.g., bias mitigation).
Scaling prompt engineering for enterprise applications.
Future trends: prompt engineering with multimodal AI (text, images, etc.).
Activities:
Work on a capstone project: Build a prompt-based solution for a real-world problem (e.g., a chatbot, content generator).
Discuss ethical implications of prompt design in a group setting.
Assignment: Submit a capstone project with documentation of prompt design, testing, and results.
Resources: Advanced prompt engineering blogs, ethical AI guidelines.
Assessment and Certification
- Weekly Quizzes: Multiple-choice and short-answer questions to reinforce concepts (10% of grade).
- Assignments: Practical tasks to design and optimize prompts (40% of grade).
- Capstone Project: A final project showcasing a complete prompt-based solution (50% of grade).
- Certification: Earn a “Prompt Engineering Mastery” certificate upon achieving a passing grade (70% or higher).
Tools and Platforms
- AI Platforms: Access to free AI tools like Grok (via x.ai), ChatGPT, or open-source models.
- Collaboration Tools: Discussion forums, live Q&A sessions (optional).
- Supplementary Tools: Text editors, JSON validators, and prompt testing environments.
Additional Resources
- Recommended readings: Blogs, research papers, and documentation on LLMs and prompt engineering.
- Community: Join a prompt engineering community for peer feedback and collaboration.
- Templates: Access a library of prompt templates for various use cases.
Instructor Support
- Weekly office hours (live or recorded).
- Feedback on assignments and capstone project.
- Access to a community of learners for networking and collaboration
