Prompt Engineering

Why This Matters  

• AI models are only as good as the prompts you provide.
• Many people struggle with getting consistent, high-quality responses from AI.
• The challenge is to craft structured, well-defined prompts that guide the model effectively.

"The first time I used ChatGPT for research and GenAI, I got vague and generic results. But after learning how to refine my prompts, I could generate specific, high-value insights and content, saving me hours of work."

The Core Idea or Framework

My Prompt Engineering Playbook is based on six essential elements that create precise and effective prompts:

1. Context or Persona – Set the background and role of the AI.
2. Task Definition – Clearly define what you want the AI to do.
3. Examples – Provide sample responses or structure to guide output.
4. Expected Behavior or Constraints – Specify limitations, tone, or format.
5. What is Considered Good – Define quality criteria for a great response.
6. Expected Steps – Break down the process to guide the AI’s logic.

If you’ve read my Second Brain Playbook you’ll recognize these 6 essential elements as my Second Brain is structured in a similar manner.

You don’t always need all six elements. But you should consider all of them and know when you are consciously dropping an element from your prompt.

Breaking It Down – The Playbook in Action

Step 1: Define Context and Persona

• Establish the role of the AI (e.g., "Act as a technical writer for a semiconductor company").
• Set a tone and communication style (e.g., formal, conversational, concise).
• Provide background information relevant to the query.

Example:

"Act as a UX designer who specializes in accessibility and usability testing. Your task is to create a checklist for evaluating a new mobile app."

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Step 2: Craft a Clear Task Definition

• Be specific about the action required (e.g., summarize, explain, generate).
• If necessary, state the goal or expected format (e.g., a list, paragraph, code snippet).
• Avoid vague or open-ended requests that may lead to inconsistent outputs.

Example:

"Summarize this article in three bullet points, each under 20 words."

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Step 3: Provide Examples for Clarity

• AI models learn from patterns, so providing a sample response improves accuracy.
• Include desired structure or key elements in your example.
• Use comparisons if needed (e.g., “Write this in the style of Author X”).

Example:

"Here’s an example of the type of answer I need: “Insert Example”. Please follow this format in your response."

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Step 4: Set Expected Behavior and Constraints

• Define limits on length, tone, complexity, or content type.
• If needed, specify what to exclude (e.g., no jargon, no opinions).
• Guide the AI to prioritize clarity, conciseness, or specific details.

Example:

"Keep the response under 200 words. Use plain English, avoid technical jargon, and write in a confident tone."

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Step 5: Define What is Considered "Good"

• If possible, describe what makes a response high-quality.
• Compare bad responses to illustrate what to avoid.
• Ask AI to follow a proven writing framework (e.g., AIDA, PAS).

Example:

"A good summary is concise, accurate, and captures the main idea without unnecessary details. A bad summary is too long, vague, or leaves out key points."

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Step 6: Guide AI with Expected Steps

• AI models perform better with structured step-by-step guidance.
• Break complex tasks into logical, ordered steps.
• Direct the AI how to think through the problem before answering.

Example:

"First, analyze the problem statement. Then, list three possible solutions with pros/cons. Finally, recommend the best option."

Tools, Workflows, and Technical Implementation

• Prompt Testing & Optimization: OpenAI Playground, GPT-4 API, PromptLayer
• AI Content Generation: ChatGPT, Claude AI, Gemini
• Data Analysis & Automation: LangChain, LlamaIndex, AutoGPT
• Code Generation & Debugging: GitHub Copilot, Windsurf Editor by Codium, Replit

"The right tools enable more precise, repeatable prompt engineering for different AI use cases."

Real-World Applications and Impact

• Improved AI accuracy by refining prompts for specific tasks.
• Increased efficiency by structuring complex queries.
• Higher quality AI-generated content for writing, analysis, and automation.

Example:

"For a generative research project, I used structured prompts to generate initial research before conducting my own research. The AI output got me most of the answers I was looking for along with referencing sources without having to search google and sift through SEO optimized fluff.

Challenges and Nuances – What to Watch Out For

• Overly broad prompts lead to vague, generic responses.
• Excessive constraints can limit creativity and flexibility.
• Token limits and Context Windows may truncate responses—consider breaking long queries into multiple steps.

"Great prompt engineering balances precision with flexibility—too rigid, and the AI lacks creativity; too loose, and the AI becomes unfocused."

Closing Thoughts and How to Take Action

• Start refining your prompts today—test different structures and constraints.
• Use prompt frameworks like APE, CARE, and TRACE to improve output quality.
• Leverage prompt engineering for AI-powered workflows, automation, and content generation.

References

External

• Write with AI
• Factspan