12 Types of ChatGPT Prompts to use Like PRO
In the age of artificial intelligence, ChatGPT has emerged as a powerful tool for generating human-like text. This capability is driven by the use of prompts, which guide the AI in producing relevant and coherent responses.
Understanding the different types of ChatGPT prompts is crucial for anyone looking to leverage this technology effectively. In this article, we will explore the various types of prompts, their applications, and best practices for crafting them.
Whether you're a content writer, a marketer, or a developer, mastering prompt engineering can significantly enhance your interaction with AI chatbots and generative AI models.
Key Takeaways
Understanding ChatGPT Prompts
What Are ChatGPT Prompts?
ChatGPT prompts are instructions given to a large language model (LLM) to generate specific types of responses. These prompts can vary in complexity and specificity, depending on the desired outcome. The goal is to provide enough context and direction for the AI to produce a coherent and relevant response.
The Importance of Context
Providing context in prompts is crucial for generating accurate and relevant responses. Context helps the AI understand the background and specifics of the task at hand, leading to more precise outputs.
Types of Prompts
Zero-Shot Prompting
Zero-shot prompting involves providing the AI with a task without any prior examples. This type of prompting relies on the AI's ability to generalize from its training data to generate a response.
Task: {WHAT YOU WANT DONE}
Context: {WHO/WHAT/WHY, any key background}
Audience: {BEGINNER / EXEC / DEVELOPER / CUSTOMER}
Output format: {BULLETS / TABLE / JSON / STEPS / EMAIL / SCRIPT}
Constraints: {WORD COUNT, TONE, MUST/AVOID, TOOLS, SOURCES}
Success criteria: {WHAT A “GOOD” ANSWER LOOKS LIKE}Few-Shot Prompting
Few-shot prompting provides the AI with a few examples to guide its response. This approach helps in setting a pattern for the AI to follow, improving accuracy and relevance.
You are a performance copywriter.
Write {COPY TYPE} for {PRODUCT/OFFER}.
Constraints:
Hook style: {HOOK TYPE}
Benefit bullets: {COUNT}
CTA: {CTA}
Avoid: {BUZZWORDS/CLAIMS/ETC}
Examples (match the rhythm and punchiness)
Example 1
Input: {PRODUCT_1 + CONTEXT}
Output:
{COPY_1}
Example 2
Input: {PRODUCT_2 + CONTEXT}
Output:
{COPY_2}
Now write this
Input: {PRODUCT + CONTEXT}
Output:Chain-of-Thought Prompting
Chain-of-thought prompting involves guiding the AI through a logical sequence of steps to arrive at a conclusion. This approach is beneficial for tasks that require reasoning or problem-solving.
Do {TASK} and provide a clear, teachable explanation.
Requirements:
Give a concise answer.
Then give a step-by-step explanation that is easy to follow, including any intermediate results that matter.
Do not include hidden internal reasoning, only the reasoning needed for clarity.
Output:
Answer:
Explanation:Self-Consistency Prompting
Self-consistency prompting generates multiple solution paths for the same problem and picks the most consistent final answer. It is commonly used as an upgrade to step-by-step reasoning for better reliability.
You are an expert {ROLE}.
Task: {PROBLEM}
Generate {N} independent solutions.
Rules:
- Use different approaches if possible.
- For each attempt, provide: (a) short reasoning summary, (b) final answer only.
Then:
- Compare the {N} final answers.
- Choose the most consistent answer.
- Output ONLY:
1) Final Answer
2) Confidence (0–100)
3) 1–2 line justification for why this answer won.
Least-to-Most Prompting
Least-to-most prompting breaks a hard problem into simpler subproblems, then solves them in sequence, using earlier answers to help later steps.
You are {ROLE}. Solve the task using least-to-most decomposition.
Problem: {PROBLEM}
Step 1 (Decompose):
- List the smallest set of subproblems needed to solve the main problem.
- Keep each subproblem simple and answerable.
Step 2 (Solve):
- Solve subproblem 1, then 2, etc.
- Reuse prior sub-answers.
Output format:
- Subproblems:
1) ...
2) ...
- Solutions:
1) ...
2) ...
- Final answer: ...
- Quick check: ...
Self-Ask Prompting
Self-ask prompting makes the model generate follow-up questions it needs, answer them, then synthesize the final response. It can also pair well with search or retrieval when facts matter.
You are an expert {ROLE}.
Main question: {QUESTION}
First, write "Follow-up Questions:" and list 3–7 questions you must answer to solve this well.
Then write "Answers:" and answer each question (use the provided context, or note what is missing).
Then write "Final:" and provide the final answer.
Constraints:
- If a follow-up requires external data and none is provided, say: "Missing data: {WHAT}" and continue.
- Keep the final answer clean and user-ready.
ReAct Prompting
ReAct combines reasoning with actions (like using tools, search, or a knowledge base), interleaving “think and do” so the model can gather missing info and adjust.
You are {ROLE}. Use Reason + Act steps.
Goal: {GOAL}
Available tools/actions: {TOOLS_LIST}
Loop:
1) Reason: Briefly state what you will do next and why.
2) Act: Choose ONE action from {TOOLS_LIST} and specify exact input.
3) Observe: Summarize what you learned from the result.
Repeat until solved.
Final:
- Answer:
- Evidence used:
- Remaining uncertainties (if any):
Tree-of-Thoughts Prompting
Tree-of-thoughts explores multiple branches of possible reasoning paths, evaluates them, and chooses the best route (instead of following one linear chain).
You are {ROLE}. Solve using a tree of options.
Problem: {PROBLEM}
1) Generate {K} candidate "thoughts" (approaches).
2) Score each thought from 1–10 using criteria: {CRITERIA}.
3) Expand the top {TOP_M} thoughts into next-step thoughts.
4) Repeat for {DEPTH} rounds or until a solution is clear.
5) Choose best path and produce final output.
Output format:
- Candidates + scores:
- Best path (high-level):
- Final answer:
- Why this path won:
Retrieval-Augmented Prompting (RAG-style Prompting)
RAG-style prompting grounds the model on retrieved documents, then answers using only that evidence. This is a standard pattern for factual reliability.
You are {ROLE}. Answer using ONLY the provided sources.
Question: {QUESTION}
Sources:
[1] {DOC_1}
[2] {DOC_2}
...
Rules:
- If the answer is not in the sources, say "Not found in sources."
- Quote or cite the source number for each key claim.
- Do not use outside knowledge.
Output:
- Answer:
- Source citations:
- Gaps / what to retrieve next:
Self-Refine Prompting
Self-refine is an iterative pattern: generate a draft, critique it, then rewrite using the critique, often repeating for multiple rounds.
You are {ROLE}. Use iterative self-refinement.
Task: {TASK}
Constraints: {CONSTRAINTS}
Quality bar: {RUBRIC}
Round 1:
- Draft:
Round 1 Critique:
- Issues (bullet list):
- What to improve (bullet list):
Round 2:
- Revised Draft:
Stop after {ROUNDS} rounds or when rubric is satisfied.
Final output: provide only the best draft, plus a 3-bullet changelog.
Structured Output Prompting (Schema-First)
Structured output prompting forces the answer into a strict structure (often JSON), making it reliable for downstream use and automation.
Return output in EXACT JSON matching this schema:
{PASTE_JSON_SCHEMA_OR_FIELD_LIST}
Task: {TASK}
Input: {INPUT}
Rules:
- Do not add extra keys.
- Use empty strings or null when unknown (as allowed).
- Ensure enums and types match.
Return JSON only.
Program-Aided Prompting (PAL-style)
PAL-style prompting asks the model to write code (like Python) to solve parts that are error-prone in plain text, then use the computed result in the final answer.
You are {ROLE}. Use program-aided reasoning.
Problem: {PROBLEM}
Data: {DATA}
1) Write Python code to compute the answer.
2) Show the computed result.
3) Explain the result in plain English.
4) Provide the final answer.
Output format:
- Code:
- Result:
- Explanation:
- Final:
Crafting Effective Prompts
Clarity and Specificity
Clarity and specificity are key to crafting effective prompts. Vague or ambiguous prompts can lead to irrelevant or incoherent responses.
Tone of Voice and Style
The tone of voice and style of the prompt can influence the AI's response. Setting a specific tone can help in achieving the desired communication style.
Practical Tips and Examples
Actionable Tips for Prompt Engineering
Examples of Effective Prompts
Conclusion
Understanding the different types of ChatGPT prompts and how to craft them effectively is essential for leveraging AI technology. By focusing on clarity, specificity, and context, you can guide the AI in generating accurate and relevant responses.
Whether you're using AI for content writing, marketing, or data analysis, mastering prompt engineering can enhance your productivity and creativity. Start experimenting with different prompt types and techniques to unlock the full potential of ChatGPT in your projects.
Ramanpal Singh
Ramanpal Singh Is the founder of Promptslove, kwebby and copyrocket ai. He has 10+ years of experience in web development and web marketing specialized in SEO. He has his own youtube channel and active on social media platform.
