Prompt Engineering Specialization course Syllabus
Full curriculum breakdown — modules, lessons, estimated time, and outcomes.
Overview (80-120 words) describing structure and time commitment.
Module 1: Introduction to Prompt Engineering
Estimated time: 10 hours
- What is prompt engineering and why it matters in generative AI
- How prompts influence AI behavior and responses
- Real-world applications across business, education, and technology
- Fundamentals of large language model (LLM) interpretation
Module 2: Core Prompting Techniques
Estimated time: 14 hours
- Zero-shot, one-shot, and few-shot prompting methods
- Role-based prompting and instruction design
- Applying constraints and formatting rules
- Context layering for improved output quality
Module 3: Advanced Prompt Engineering Strategies
Estimated time: 16 hours
- Chain-of-thought and step-by-step reasoning prompting
- Prompt optimization for accuracy and consistency
- Handling ambiguity and hallucinations in AI responses
- Diagnosing and resolving prompt failure cases
Module 4: Prompt Engineering for Real-World Applications
Estimated time: 16 hours
- Applying prompts to coding assistance and data analysis
- Content creation and decision support use cases
- Building prompt workflows and reusable templates
- Integrating prompts into AI-powered tools and applications
Module 5: Capstone Project: Prompt Engineering Portfolio
Estimated time: 20 hours
- Design high-quality prompts for real-world scenarios
- Test, refine, and document prompt performance
- Showcase skills in a professional portfolio
Module 6: Final Project
Estimated time: 20 hours
- Deliverable 1: Collection of reusable prompt templates
- Deliverable 2: Documented testing and refinement process
- Deliverable 3: Professional portfolio showcasing prompt engineering skills
Prerequisites
- Beginner-friendly – no coding experience required
- Familiarity with basic AI and generative model concepts helpful
- Access to AI platforms like ChatGPT or similar tools
What You'll Be Able to Do After
- Understand how large language models interpret prompts
- Design clear, structured, and effective prompts for various AI tasks
- Apply zero-shot, few-shot, and chain-of-thought prompting techniques
- Evaluate and refine prompts for reliability and output quality
- Build and deploy prompt templates for business, creative, and technical use cases