🧠 Prompt Engineering: How to Talk to AI and Get What You Need

Have you ever heard the saying “garbage in, garbage out”? It applies perfectly here. If you give vague, poorly structured prompts, you’ll get equally vague or irrelevant results. Precise inputs lead to meaningful outputs — it’s that simple.

Prompt engineering is the process of designing and structuring prompts (instructions) to guide a language model like ChatGPT to generate the desired output. Whether you’re generating code, content, or business insights, a well-crafted prompt is critical.

Wide AI vs Narrow AI

Narrow AI

Narrow AI is designed to perform a specific task (e.g., facial recognition, spam detection, playing chess). It operates within a limited context.

Example: ChatGPT writing an email or generating Python code on request.

Wide AI (a.k.a. Artificial General Intelligence)

Wide AI would be capable of performing any intellectual task that a human can do. It requires reasoning, learning, and generalization across domains.

Note: Wide AI is still largely theoretical.

Core Concepts of Prompt Engineering

Mindset Checklist

  • ✅ Clear Instructions: Be specific with what you want.
  • ✅ Adopt a Persona: Give the AI a role (e.g., “You are a senior Python developer…”).
  • ✅ Specify the Format: Define how the output should be structured.
  • ✅ Avoid Leading Answers: Ask open-ended or balanced questions.
  • ✅ Limit Scope: Keep requests focused.

Practical Prompt Examples

1. Business Prompt (Manager)

“Write a concise email summarizing this week’s sales figures, highlighting areas for improvement and praising the team’s achievements.”

2. Coding Prompt

Write a Python function to filter out ‘age’ values from a list of objects and explain the code with a use-case.

3. Developer Persona Prompt

You are a senior NodeJS tutor helping a Python developer learn NodeJS from scratch. Answer in plain, step-by-step language.

4. Roleplay + Instruction Prompt

You are a DevOps consultant helping a startup implement IaC with Terraform. Please confirm you understand the task.

5. Markdown + Reasoning Prompt

Write a Markdown-formatted list of which AWS services to use when building a serverless architecture, including the reasoning.

6. Boilerplate Prompt for Teams

You are an experienced Python developer. Write a simple script that processes CSV files and prints summary statistics. Please explain each part of the code in plain language.

7. Applied Role-Based Prompt (Advanced)

You are a Senior Python Developer at a healthcare organization. The company is building cloud-native applications using AWS, and your primary role is to lead the development of secure, modular, and well-documented Python code. You are responsible for backend API services, data processing scripts, and integration logic for various AWS services including Lambda, DynamoDB, S3, and CloudWatch. The frontend team uses React, and your services power key workflows in a Spring-based backend environment. Your work must comply with healthcare data regulations like HIPAA. In addition to writing code, enforce clean architecture principles. Can you help? If you understand, please confirm from now on by replying ‘Confirmed’ when asked a question.

Explanation: This updated prompt focuses on:

  • A clear software development role
  • Realistic project stack and team collaboration
  • Practical responsibilities (backend, APIs, data workflows)
  • Security and compliance in a regulated sector

These role-specific prompts help generate accurate, context-rich Python code in collaborative product teams. They support the creation of production-grade code in high-stakes environments where accuracy, compliance, and domain expertise matter.

Why It Matters in Daily Workflow When I need to understand a complex subject, I break it down into smaller chunks and ask the model to explain each part as if I were a beginner. I keep technical terms to a minimum and ask it to gradually assess my understanding as I ask more advanced questions. This method helps me level up quickly and adapt to challenging topics.

As a Lead DevOps Engineer, I use structured prompts like this daily. Compared to tools like Cursor or static templates, prompt engineering gives me:

  • 💡 Greater flexibility: I can adapt instructions mid-session without switching tools.
  • ⚡ Faster feedback loops: Results come instantly, iteratively improving as I tweak.
  • 🧠 Deeper reasoning: LLMs process context better when the prompt is role-aware and scoped.
  • 📋 Improved documentation: These prompts double as reusable task briefs for training and ops.

Prompt engineering isn’t just a trick — it’s my workflow accelerator.

Summary of Prompt Examples (from promptingguide.ai)

(Each under 20 words)

  • Ask the AI to summarize a long passage.
  • Use “step-by-step” to encourage logical reasoning.
  • Ask the model to debate or argue both sides.
  • Ask the AI to critique its own answer.
  • Use multiple examples to set a pattern.
  • Assign roles like “act as a lawyer.”
  • Provide format: bullet list, JSON, etc.
  • Break down problems into smaller parts.
  • Use constraints like “under 50 words.”
  • Use prior answers as input to new prompts.
[Prompt Engineering Guide Prompt Engineering Guide](https://www.promptingguide.ai/)

Summary (What is Prompt Engineering?)

Prompt engineering is the practice of writing effective instructions for AI models so they produce precise, useful outputs. It blends creativity with technical clarity. A good prompt improves accuracy, reduces hallucination, and unlocks the full potential of language models. It’s especially powerful in roles where speed, clarity, and scale matter—like DevOps, content creation, coding, or education. By learning prompt engineering, you’re not just using AI. You’re shaping how it thinks and responds.

Stay tuned for future posts where we dive deeper into LangChain, agents, and how prompt design impacts model behavior in real-world applications.

Thanks for reading, Dave

Written on June 16, 2025