I. Foundational Prompting
These are the simplest and most commonly used prompt styles.
1. Zero-shot Prompting
What it is: Ask the model to perform a task with just instructions, no examples.
Example Prompt:
Classify the sentiment of this review:
“This movie was visually stunning but lacked depth.”
Expected Output:
Neutral
Use Case: Quick classification, summarization, or Q&A when the task is simple and well-defined.
2. One-shot Prompting
What it is: Give one example to guide the model’s response.
Example Prompt:
EXAMPLE:
Review: “The movie was amazing and heartwarming.”
Sentiment: PositiveReview: “This movie was visually stunning but lacked depth.”
Sentiment:
Expected Output:
Neutral
Use Case: Slightly complex tasks where one example can clarify the expected output.
3. Few-shot Prompting
What it is: Provide several examples to help the model learn a pattern.
Example Prompt:
Review: “Great acting, loved it!” → Positive
Review: “Meh, not bad but not great.” → Neutral
Review: “Terrible pacing and plot.” → NegativeReview: “Visually stunning but lacked depth.” → ?
Expected Output:
Neutral
Use Case: Tasks requiring more nuance (e.g., tone detection, style generation, structured data extraction).
II. Instruction Structuring Prompts
These guide how the model behaves and formats its output.
4. System Prompting
What it is: Define strict instructions for format, style, or logic.
Example Prompt:
Classify the sentiment. Return the result as uppercase text only.
Review: “Beautiful story with rich characters.”
Sentiment:
Expected Output:
POSITIVE
Use Case: API integrations, consistent output formatting, enforcing constraints.
5. Role Prompting
What it is: Tell the model to “act as” a specific role or persona.
Example Prompt:
You are a career coach.
A client says: “I feel stuck in my job.”
How would you respond?
Expected Output:
“It’s totally normal to feel stuck sometimes. Let’s explore what energizes you…”
Use Case: Emulating expertise, adjusting tone/style (e.g., teacher, doctor, developer).
6. Contextual Prompting
What it is: Provide background context to inform better responses.
Example Prompt:
Context: This is for a blog about 80s arcade games.
Suggest 3 article ideas.
Expected Output:
- The History of Pac-Man
- How Donkey Kong Changed Gaming
- Top 10 Forgotten Arcade Cabinets
Use Case: Use with long-form tasks, branding, personalized content generation.
III. Reasoning & Problem Solving Prompts
Used when logic or multi-step thinking is needed.
7. Step-back Prompting
What it is: Ask a general or reflective question first, then solve the main task.
Example:
Step 1 Prompt:
What are key elements that make a good game storyline?
Output:
Conflict, character growth, high stakes…
Step 2 Prompt:
Now write a level story for a game using these.
Use Case: Idea generation, brainstorming, creative writing, complex tasks.
8. Chain of Thought (CoT)
What it is: Ask the model to think step by step before giving an answer.
Example Prompt:
I was 3 years old, and my partner was 3 times my age. Now I’m 20. How old is my partner? Let’s think step by step.
Output:
- At 3, partner was 3 × 3 = 9.
- Age difference is 6.
- Now I’m 20, so partner is 20 + 6 = 26.
Use Case: Math, logic, coding, multi-step reasoning.
9. Self-consistency
What it is: Generate multiple CoT outputs and pick the most frequent answer.
Prompt (run multiple times):
Same as CoT example above.
Output:
Majority of outputs = “26”
Use Case: When accuracy is more important than speed; especially useful in QA systems.
10. Tree of Thoughts (ToT)
What it is: Explore multiple reasoning paths in a decision tree format.
Prompt:
Think of 3 possible ways to solve this mystery. Then narrow it down to the most logical path.
Output:
- Path A → clues don’t match
- Path B → possible, but lacks motive
- Path C → strong evidence and motive → chosen
Use Case: Strategic planning, game design, decision-making problems.
IV. Action-Oriented / Agentic Prompts
Combine reasoning with interaction or tool usage.
11. ReAct (Reason + Act)
What it is: Model reasons, performs actions (like searching), and reflects.
Prompt (with tools):
How many kids do the members of Metallica have?
Steps:
- Reason: Identify band members
- Act: Search each one’s number of kids
- Reason again: Add up
- Final Answer: 10
Use Case: Research agents, personal assistants, API-powered workflows.
V. Prompt Automation & Meta-Prompting
Helps generate better prompts using the LLM itself.
12. Automatic Prompt Engineering (APE)
What it is: Ask the model to create or rewrite prompts.
Prompt:
Write 10 different ways to say: “I want a Metallica t-shirt, size small.”
Output:
- I’d like to buy a small Metallica tee.
- One Metallica shirt, size S, please.
- Can I order a Metallica shirt in small?
Use Case: Chatbot development, UX testing, prompt optimization.
VI. Developer & Code Prompts
Targeted at software development tasks.
13. Write Code
Prompt:
Write a Bash script to rename all files in a folder by adding “draft_” as a prefix.
Output:
Shell script with mv loop
14. Explain Code
Prompt:
Explain what this Bash code does:
(…script here…)
Output:
Step-by-step explanation of each line
15. Translate Code
Prompt:
Translate this Bash code to Python.
Output:
Python script using os and shutil libraries
16. Debug/Review Code
Prompt:
Review this code and point out any bugs or improvements.
Output:
- Suggests edge case handling
- Notes unused variables
- Recommends optimizations
🔍 What is Prompt Engineering?
Prompt Engineering is the process of designing, testing, and refining prompts to get accurate, relevant, or creative outputs from AI models like ChatGPT or Claude.
It’s half language, half logic — combining understanding of how language models “think” with creative problem-solving and domain-specific knowledge.
🧠 Key Differences of Prompt Engineering
- Low-Code to No-Code Approach
You don’t always need deep ML or stats knowledge. It’s more about knowing how to ask the model well. - Communication + Technical Mindset
Unlike traditional roles that require programming-heavy work, prompt engineers often use natural language as a tool. - Rapid Experimentation
Prompt engineering feels more like design thinking: try, test, tweak. - Cross-disciplinary
Useful in law, finance, education, software, and customer support — wherever LLMs are being used.
Comparison with Other Data Science-Related Jobs
| Role | Main Skills | Tasks | Tools/Tech | Key Differences |
|---|---|---|---|---|
| Prompt Engineer | NLP understanding, creative thinking, logic, domain knowledge | Crafting effective prompts for LLMs, automating workflows, tuning for performance | ChatGPT API, LangChain, OpenAI Playground | Focuses on language models rather than traditional data pipelines |
| Data Analyst | SQL, Excel, basic Python, data visualization | Data cleaning, report generation, dashboard building | Excel, Power BI, Tableau, SQL | Works mainly on structured data, less model-related |
| Data Scientist | Statistics, Python/R, ML, storytelling | Building predictive models, A/B testing, feature engineering | scikit-learn, pandas, TensorFlow, Jupyter | Works heavily with data and statistical models |
| Machine Learning Engineer | Software engineering, ML frameworks | Building ML systems, deploying models | TensorFlow, PyTorch, Docker, Kubernetes | More engineering focus, builds pipelines for ML models |
| NLP Engineer | Deep NLP knowledge, tokenization, fine-tuning | Building custom NLP models, entity recognition, summarization | Hugging Face, spaCy, BERT/GPT | Works more on model internals, not just prompting |
| AI Product Manager | Strategy, UX, AI basics | Defining AI product features, managing LLM use cases | Jira, Figma, product strategy tools | More business- and customer-focused, not hands-on with prompts |
