🧠 Explaining Types of Prompt Engineering

Prompt engineering is the secret sauce behind getting powerful, reliable results from Large Language Models (LLMs) like GPT-4, Claude, or Mistral. But did you know there are different types of prompt engineering techniques, each suited for specific tasks?

In this post, we’ll walk through the main types of prompt engineering, how they work, and when to use each.

Types of Prompting

🚀 What Is Prompt Engineering, Again?

Prompt engineering is the practice of crafting inputs (or prompts) that guide an LLM’s behavior. Think of it as telling a really smart assistant how to think, what context to use, and what kind of output you want.

Different scenarios need different types of prompting—and that’s where these categories come in.

🔢 1. Zero-Shot Prompting

📌 What It Is:

You ask the model to perform a task without giving any examples.

🧠 Example:

Translate “Bonjour” to English.

✅ Use When:

• The task is common or simple

• You want quick answers

• You’re exploring capabilities

✌️ 2. Few-Shot Prompting

📌 What It Is:

You provide a few examples of input-output pairs before asking the model to continue the pattern.

🧠 Example:

Translate the following:

French: Bonjour → English: Hello  
French: Merci → English: Thank you  
French: Au revoir → English:

→ Model completes: "Goodbye"

✅ Use When:

• You want to show the model a pattern

• Task requires structure or nuance

• Output format matters

🧵 3. Chain-of-Thought (CoT) Prompting

📌 What It Is:

You encourage the model to think step-by-step to improve reasoning and accuracy.

🧠 Example:

Q: If there are 5 apples and you eat 2, how many are left?  
A: Let's think step by step. There were 5 apples. I ate 2. So, 5 - 2 = 3 apples left.

✅ Use When:

• You need logical reasoning

• Tasks involve math, planning, or multiple steps

🧩 4. Role-Based Prompting

📌 What It Is:

You assign a persona or role to the model to steer tone, behavior, and style.

🧠 Example:

You are a professional resume writer. Rewrite the following summary to make it more compelling...

✅ Use When:

• You want the model to adopt a specific tone or voice

• Building chatbots, assistants, customer support

🧱 5. Instruction-Based Prompting

📌 What It Is:

You give direct, explicit instructions about what the model should do.

🧠 Example:

Summarize the following article in 3 bullet points.

✅ Use When:

• You want structured or formatted outputs

• The task is open-ended or creative

🔐 6. Delimiter-Based Prompting

📌 What It Is:

You use special characters (like """ or ###) to clearly separate sections or inputs.

🧠 Example:

Summarize the following text:
"""Photosynthesis is the process..."""

✅ Use When:

• Dealing with long or messy input

• You want to avoid confusion between input/output

⚙️ 7. Multi-Turn Prompting (Contextual Prompting)

📌 What It Is:

You maintain a back-and-forth conversation with memory of previous inputs.

🧠 Example:

User: What's the capital of France?Model: ParisUser: What’s the population?Model: [Remembers you're talking about Paris]

✅ Use When:

• You’re building a chatbot

• Long, contextual tasks

🎯 Choosing the Right Type

Use Case	Prompting Style
Quick Q&A	Zero-shot
Pattern replication	Few-shot
Logic & math	Chain-of-Thought
Custom tone/voice	Role-based
Instructional tasks	Instruction-based
Large text input	Delimiter-based
Conversational agents	Multi-turn/contextual

🏁 Final Thoughts

Prompt engineering isn’t just one skill—it’s a toolbox full of strategies. The more types you know, the more control you have over the LLM. Try combining types too—for example, role-based + few-shot + CoT—to get even better results.