Description

Introduction
As prompt engineering evolves beyond the basics, structuring prompts effectively becomes critical to achieving reliable, high-quality outputs. This course dives into intermediate-level techniques, helping you organize prompt inputs, apply logical flow, and use frameworks to improve consistency, depth, and relevance in AI-generated responses.

Prerequisites
Basic experience with prompt engineering or AI tools like ChatGPT
Familiarity with single-turn and multi-turn prompts
Comfort with experimenting and analyzing different prompt outcomes

Table of Contents

1. Recap of Prompt Engineering Fundamentals
   1.1 Prompt Types: Instructional, Few-shot, and Zero-shot
   1.2 Understanding Input-Output Behavior of LLMs
   1.3 Common Prompting Challenges at the Basic Level

2. Structured Prompting Principles
   2.1 Importance of Prompt Structure in Output Quality
   2.2 Logical Sequencing and Clarity
   2.3 Controlling Tone, Style, and Format

3. Building Modular Prompts
   3.1 Breaking Down Tasks into Components
   3.2 Chaining Prompts for Complex Processes
   3.3 Reusable Prompt Templates

4. Role-Based Prompting for Context Control
   4.1 Assigning Roles to Guide Response Styles
   4.2 Combining Roles and Instructions Effectively
   4.3 Role-Switching Across Dialogue

5. Enhancing Output Specificity
   5.1 Using Constraints and Examples Strategically
   5.2 Precision Prompts for Technical or Factual Tasks
   5.3 Avoiding Overgeneralization

6. Multi-Turn Interactions and Follow-Up Prompts
   6.1 Maintaining Consistency Across Conversations
   6.2 Tracking Context in Long Prompt Threads
   6.3 Repair Strategies for Misaligned Responses

7. Debugging and Refining Prompts
   7.1 Common Structural Prompting Errors
   7.2 Iteration Techniques for Improved Performance
   7.3 Using Output Feedback Loops

8. Frameworks and Patterns
   8.1 The TREE Method (Task, Role, Examples, Execution)
   8.2 MECE Structuring for Clarity
   8.3 Prompt Pattern Libraries and Blueprints

9. Use Cases for Structured Prompts
   9.1 Summarization and Analysis Tasks
   9.2 Creative Generation with Structure
   9.3 AI-Assisted Decision Making

10. Evaluating Prompt Success
    10.1 Output Quality Metrics
    10.2 Human Review and AI Evaluation
    10.3 Prompt A/B Testing Techniques

Structured prompting enables more control, reliability, and depth in AI-generated outputs. With intermediate techniques like modularization, role-based prompting, and task sequencing, users can unlock greater value from language models. This level of prompt engineering bridges the gap between casual use and professional application, empowering users to design with purpose and precision.