🎯 Mastering Prompt Styles: A Straightforward Guide That’ll Stick With You Forever

If you’ve ever played around with AI models like ChatGPT, Claude, or LLaMA, you’ve probably realized this: the way you talk to the model changes everything. It's not just about what you ask — it's how you ask it. That’s where prompt engineering comes into play.

There are many styles and types of prompts, and knowing when and how to use them is the difference between a dull answer and a brilliant one.

Let’s dive into the most common prompt styles and types — no fluff, just pure insight — and make sure you never forget them.

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🧩 Prompt Styles — How You Frame the Conversation

1. Alpaca Prompt

Think of this as giving the AI a neatly packaged task: an instruction, some optional context (input), and then letting it respond.

Structure:

Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.

### Instruction:
[INSTRUCTION]

### Input:
[INPUT] // optional

### Response:

Example:

### Instruction
"Create a list of three open-ended and engaging questions formatted as a single string. Each question should be separated by '||'. These questions are for an anonymous social messaging platform, like Qooh.me, and should be suitable for a diverse audience. Avoid personal or sensitive topics, focusing instead on universal themes that encourage friendly interaction.

### Response:
What’s a hobby you’ve recently started?||If you could have dinner with any historical figure, who would it be?||What’s a simple thing that makes you happy?"

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2. INST Format (Used by LLaMA-2)

This one is clean and controlled. It gives the model a system message (optional), the user's prompt, and expects a smart response.

Structure:

[INST] <> {{system message}} <> {{user message}} [/INST] {{model response}}

Example:

[INST] <>
You are a helpful, respectful and honest assistant. Always answer as helpfully as possible, while being safe. Avoid harmful, biased, or false content.
<>
There's a llama in my garden 😱 What should I do?
[/INST]

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3. ChatML (OpenAI Format)

Designed for chat-based models, this uses simple tags to handle multi-turn conversations.

Structure:

<|im_start|>role
Message content
<|im_end|>

Example:

<|im_start|>system
Provide some context and/or instructions to the model.
<|im_end|>
<|im_start|>user
The user’s message goes here
<|im_end|>
<|im_start|>assistant

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🧠 Prompting Types — How You Guide the Model

1. Zero-Shot Prompting

No examples, just a straight-up task.

Example:

Summarize the below log entries for me

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2. Few-Shot Prompting

You show the model a couple of examples first. It’s like saying, “Here’s how I want you to do it.”

Example:

You are an AI Assistant who is specialized in maths.
Only solve math problems.

Example:
Input: 2 + 2
Output: 2 + 2 is 4 which is calculated by adding 2 with 2.

Input: 3 * 10
Output: 3 * 10 is 30 which is calculated by multiplying 3 by 10.

Input: Why is the sky blue?
Output: Bruh? You alright? Is it maths query?

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3. Chain of Thought (CoT) Prompting

This is your go-to for complex reasoning. You guide the model to think step-by-step before answering.

Example:

You are an expert in breaking down complex problems.

For a given input:
1. Analyse
2. Think (multiple times)
3. Output
4. Validate
5. Result

Output Format:
{ step: "string", content: "string" }

Input: What is 2 + 2?
Output: { step: "analyse", content: "The user is asking a basic arithmetic question." }
Output: { step: "think", content: "Addition is performed from left to right." }
Output: { step: "output", content: "4" }
Output: { step: "validate", content: "4 is correct." }
Output: { step: "result", content: "2 + 2 = 4." }

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4. Self-Consistency Prompting

Let the model generate multiple answers and pick the most consistent one.

Example:

Q: When I was 6, my sister was half my age. Now I’m 70, how old is she?

Answer 1: When I was 6, she was 3. Now I’m 70, so she’s 70 - 3 = 67.
Answer 2: Same logic — 67.
Answer 3: (Incorrect) 70 / 2 = 35.

Final Answer: 67

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5. Instruction Prompting

Clear, direct instructions = better results.

Instead of this:

How can I save money?

Use this:

Provide 3 specific strategies for reducing monthly household expenses, focusing on groceries and utilities.

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6. Direct Answer Prompting

Cut the noise. Just give the answer.

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7. Persona-Based Prompting

Tell the model who it is. That changes how it thinks.

Example:

You are an experienced travel guide. Plan a Kyoto trip with cultural landmarks, traditional food, and must-see spots.

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8. Role-Playing Prompting

Similar to persona-based, but interactive. You assign a role and let the model act it out.

Example:

AI-Mentor: A friendly mentor guiding a student through a negotiation exercise. Ask the student about their experience and provide feedback.

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9. Contextual Prompting

Give the model some background before asking the question. Like giving it a map before the journey.

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10. Multimodal Prompting

When it’s not just text — images, charts, or even sound can be used alongside prompts. (Yes, that’s real.)

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🧠 Final Thought: Make It Stick

If there’s one thing to take away from all this:

How you talk to an AI matters more than you think.

It’s not magic. It’s communication.
And like any great conversation, it depends on tone, context, and clarity.

So the next time you write a prompt, ask yourself:

• Am I being clear?

• Am I using the right structure for the job?

• Am I giving enough guidance (or just enough freedom)?

Master these styles and types, and you won’t just get better AI outputs —
you’ll start thinking more clearly yourself.

Now go, prompt like a pro. 🧠💡