Prompt Structure
The core components needed for effective prompts and their suggested balance.
Introduces the four essential components of a high-quality prompt: Context, Output Format, Task, and Constraints. Users can adjust sliders to visualize how emphasizing each component affects prompt behavior. The visual bars and tips help learners grasp how to craft more accurate, structured, and goal-oriented prompts based on their intended outcome. It's a hands-on tool for mastering prompt anatomy.
Adjust Component Weights:
Components Breakdown:
| Component | Description |
|---|---|
| Information (Context) | Foundational background (domain data, goals, context) the model needs for accuracy and relevance. |
| Output Format | Structural cue (table, list, summary) defining how the answer should appear for readability and workflow efficiency. |
| Task (Goal) | Clear instructions (summarize, analyze, compare) using action verbs, often with constraints (word count, tone). |
| Constraints and Tone | Safety instructions, preferred tone (formal, casual), or audience alignment; essential for compliance or brand voice. |
“Adding contextual awareness and domain-specific knowledge to prompts can significantly improve the relevance and accuracy of AI-generated responses.”
“Specifying the format ensures each stage of recursive prompting builds logically, resulting in a coherent product.”
Effective Prompt Patterns
Proven methods to structure prompts for different types of tasks and outcomes.
Showcases a curated set of repeatable prompt structures, from role-based prompts to chain-of-thought and adversarial testing. Each pattern includes a short description and example, making it easy to grasp when and how to apply them. It's especially useful for those looking to apply tested techniques to enhance reliability and consistency across different use cases.
Common Patterns:
| Pattern | Description | Example |
|---|---|---|
| Role-Based Prompting | Assigns a persona/expertise to steer tone and reasoning. | “You are a Renaissance art historian...” |
| Iterative Refinement | Improves responses via targeted follow-ups. | “Draft an article. Now revise it...” |
| Chain-of-Thought | Encourages structured, logical steps for complex tasks. | “Explain step-by-step how to calculate...” |
| Constraint-Based Prompting | Applies strict format, style, or length limits. | “Explain neural backpropagation in exactly three sentences...” |
| Task Clarification + Format | Separates Context, Task, Format for clarity. | “Context: You are a tech reviewer. Task: Compare iPhone... Format: Two-paragraph...” |
| Few-Shot Prompting | Provides input-output examples for the model to follow. | “Here’s how we usually write client emails...” |
| Zero-Shot Prompting | Gives clear instructions without examples. | “Draft a professional thank-you email...” |
| Adversarial Testing | Challenges the model with tricky prompts to test robustness. | “Is Paris the capital of France or...” |
| Self-Consistency | Prompts multiple solutions to find the most reliable answer. | “Solve 2x + 3 = 11 using at least two methods.” |
Vibe Coding
A natural-language approach to build simple software without writing traditional code.
What It Is:
A way for non-engineers to create lightweight, single-use software using prompts. Ideal for MVPs, personal tools, and creative automation.
Key Concepts:
- Prompt-to-code: "Create a task tracker..."
- Conversational debugging to refine.
- No programming knowledge needed.
- Emphasizes experimentation.
Example Projects:
🌐 Generative AI Professional Prompt Engineering Guide – Website
📋 Generative AI Professional Prompt Engineering – Cheat Sheet
📂 Kanban Board
🔄 Currency Converter GUI
Best For:
Creatives, educators, analysts; prototyping, internal automation, teaching tools.
⚠️ Cautions:
- AI code may lack security, structure.
- Always test before real use.
- Not for production-scale software.
AI Learning Platform
Using AI tools to turn static content into an interactive learning system.
Highlights how to create an AI-augmented educational platform using tools like NotebookLM, Illuminate, and custom GPTs. These tools enable learners to explore, interact with, and personalize their learning journey. It’s ideal for those designing self-paced or collaborative learning environments powered by AI.
Core Tools:
- 🧠 NotebookLM: Create research-linked knowledge bases, map idea relationships. “Organize and explore interconnected ideas for deeper understanding.”
- 🎙️ Illuminate: Convert static content into dynamic AI discussions. “Facilitates real-time, AI-enhanced dialogue that brings static ideas to life.”
- 🤖 Custom GPTs: Train a personalized tutor using specific content. “Gain access to a personalized tutor capable of answering questions and offering clarifications.”
Best Use Cases:
- Self-paced study
- Research exploration
- Collaborative learning
- Context-aware Q&A
- AI-augmented training
“This synergy turns the Guide into a living, breathing platform for interactive learning and professional development.”
Use Cases
Examples of applying prompt engineering across different domains.
Offers practical examples of prompt engineering in business, education, and technical fields. Shows how prompts are used for SEO writing, training bots, legal summarization, and more. A great reference for anyone looking to understand the cross-industry applications of AI prompting.
Applications:
| Business | Education | Technical |
|---|---|---|
| 📋 Employee training | 🧑🏫 Teaching assistant bots | 🧪 Scientific report summarization |
| ✍️ SEO content generation | 📚 Interactive learning paths | ⚖️ Legal document drafting |
| 📝 Meeting note condensation | 📖 Study aids with examples | 📊 Data analysis prompt scripting |
| 🛍️ Product/service descriptions | 🎓 GPT-powered tutoring assistants | 🔎 Knowledge retrieval applications |
Output Formats
Defining how the AI should structure and present its response.
Specifies response structure, style, organization. Clear formats improve usability, accuracy, and workflows.
Common Formats:
- 📊 Table: Comparisons, structured data. Ex: "List top 5 products... in a table."
- • Bullet List: Clear, scannable points. Ex: "List marketing benefits as bullet points."
- 📝 Executive Summary: Concise overview. Ex: "Summarize report findings..."
- 🖼️ Slide-ready Text: For presentations (headings, short points).
- 📈 Charts / Markdown: Visuals, documentation, wikis, tutorials.
- 🔧 JSON / YAML: Structured outputs for software/APIs.
- 📄 Structured Docs: SOPs, NDAs, legal drafts. Ex: "Draft an NDA with clauses..."
“Defining the output format ensures that each stage of recursive prompting builds logically, resulting in a coherent final product.”
Prompting Techniques
Fundamental methods repeatedly emphasized for effective prompt engineering.
Summarizes foundational prompting principles like being specific, assigning roles, and using step-by-step logic. Includes concise descriptions and examples to help users refine their prompting approach. These techniques are essential for improving the quality, reliability, and adaptability of AI-generated content.
Key Techniques:
| Technique | Description | Example Prompt |
|---|---|---|
| Be Specific | Clear instructions (scope, outcome, format). | “List 3 actionable marketing tips for Q4.” |
| Step-by-Step | Encourages logical breakdown for coherence. | “Let’s think through this step-by-step...” |
| Assign a Role | Frames AI identity for style/tone shift. | “Acting as an HR executive, write...” |
| Clarify Output Format | Specifies structure (JSON, table, list). | “Output as a bullet list with subheadings...” |
| Iterative Prompting | Prompts designed for refinement stages. | “Here’s a draft. Now revise to be more...” |
| Comparative Prompting | Generates side-by-side evaluations. | “Compare the pros and cons of Slack vs. Teams...” |
| Provide Examples | Uses few-shot prompts for style/tone guidance. | “Here’s how we reply to support tickets...” |
| Structured Follow-Ups | Encourages AI to guide next actions/ask questions. | “What 3 questions should I ask to improve this prompt?” |
“Refining and iterating prompts is not optional—it’s essential for achieving reliable, high-quality results.”