ChatGPT Prompts Cheatsheet - Effective Prompt Writing Techniques
Essential ChatGPT prompt templates organized by Role Setting, Structured Output, Code, Text Processing, Few-shot, and Chain of Thought. Copy a template, fill the variables, and ship.
Back to AI Prompts#Role Setting (System Role)5
You are a senior {domain} expert with 10+ years of experience. Answer the following in a professional yet accessible way: {question}Anchor expertise with domain + years, avoids generic answers, good for consulting
As a {role}, complete the following task strictly following {standard/spec}: {task}Force compliance with a spec (PEP8, RFC, company template), reduces rework
Your identity is {role}, your goal is {goal}, your constraints are {constraints}. Begin: {input}Identity + goal + constraints triple, clear behavior boundaries for complex tasks
Analyze {issue} from the perspective of {role}, give 3 viewpoints from different angles with supporting reasons.Force multi-angle thinking, avoids single-viewpoint bias, good for decisions
Assume you are a {position} in {scenario}; give the complete steps and caveats to handle {issue}.Scenario-based role-play, output closer to real workflows, good for process design
#Structured Output (JSON/Table/Markdown)5
Output the following as JSON with fields: name, description, tags. Output JSON only, no explanation. Content: {text}Named fields + no explanation, get directly parseable JSON for programmatic use
Compare {A} and {B} in a Markdown table across: performance, usability, ecosystem, learning cost.Predefined dimensions, doc-ready table output, good for tech selection
Output in this structure: ## Title / ### Subtitle / - Points. Topic: {topic}Specify heading levels, paste-ready Markdown, good for quick doc skeletons
Output strictly per this JSON Schema, with no explanation: {schema}. Input: {input}Schema constrains field types, good for generating validated API data
Break the following requirement into a checklist table with columns: task, priority, estimated hours. Requirement: {req}Requirement to actionable list, good for planning and task assignment
#Code (Generate/Review/Explain/Refactor)5
Implement the following in {language}: {requirement}. Include error handling, comments, and a unit test example.Require error handling + comments + tests at once, avoids "toy code"
As a senior {language} developer, review the following code for bugs, performance, and readability, then provide an improved version:
```
{code}
```Categorized review (bug/perf/readability) + improved version beats generic "check this"
Explain the following code line by line: its purpose, mechanism, and potential issues: {code}Line + mechanism + risk, good for onboarding to unfamiliar codebases
Refactor the following code to reduce complexity and improve readability while preserving behavior. Explain each change: {code}Stress "preserve behavior" to avoid logic changes; require explanations for review
Write unit tests for the following function covering happy path, edge cases, and exceptions: {function}Explicit three test categories, avoids happy-path-only tests
#Text Processing (Summarize/Translate/Rewrite/Extract)5
Summarize the following in under 200 words and extract 3-5 keywords: {long text}Word limit + keyword extraction, good for skimming long articles
Translate the following into {target language}, keeping technical terms accurate and tone {formal/casual}: {text}Specify term accuracy and tone, avoids machine-translation feel
Rewrite the following in {style: explainer/academic/marketing} style for {audience}: {text}Style + audience dual constraint, output hits the scenario precisely
Extract all {entity type: person/place/date/amount} from the following as a list: {text}Specify entity type, good for NER labeling or info gathering
Compare the differences between the two texts across viewpoint, evidence, and conclusion: {text A} vs {text B}Structured comparison dimensions, good for version diffs and argument analysis
#Few-shot Prompting4
Examples:
Input: good → Output: great
Input: bad → Output: terrible
Now process: {input}2-3 examples demonstrate the mapping; model generalizes automatically, good for sentiment shifts
Task: classify customer reviews as positive/negative/neutral.
Examples: "Works great" → positive
"Slow shipping" → negative
Classify: {review}Examples + label set, higher accuracy than zero-shot classification
Generate SQL in this format:
Q: list all users
A: SELECT * FROM users;
Q: {natural language query}
A:Q&A pairs demonstrate format, common for Text-to-SQL and structured generation
Style imitation: here are samples of my writing style
{sample 1}
{sample 2}
Continue in the same style: {opening}Provide style samples so the model imitates personal writing style
#Chain of Thought4
Reason through the following step by step: list known conditions, then analyze approach, then give the answer: {question}Force stepwise reasoning, significantly improves math/logic accuracy
Solve the following step by step like a math teacher, citing the formula or theorem for each step: {question}Require citing basis, makes it easy to verify the reasoning
Before answering, think: 1) key constraints 2) feasible options 3) pros/cons of each 4) recommended option. Question: {question}Four-step decision framework, good for open-ended decisions
Perform a pros/cons analysis on this decision: list all factors first, then weigh them, then recommend: {decision}List factors before weighing, avoids bias, good for major decisions
💡 Tips
- Providing role + constraints + output format together beats "help me write X" by an order of magnitude.
- When requesting JSON, always add "output JSON only, no explanation" or the model prepends prose and breaks parsing.
- For complex reasoning, trigger Chain of Thought with "step by step"; accuracy on math/logic improves markedly. On GPT-4+ you can literally write "Let's think step by step".
- 2-3 Few-shot examples are enough; more risks overfitting and wastes tokens. Keep example I/O format identical to the real task.