50 Best Claude Prompts That Get Exceptional Results (2026) | AI Prompts Pro

50 proven Claude prompts for writing, coding, analysis, research, and business. Includes system prompts, XML formatting tips, and why Claude responds differently than ChatGPT.

2026-02-19

50 Best Claude Prompts for Every Use Case (2026)

Max Sterling

February 19, 2026 · 18 min read

Published February 19, 2026

If you've been using ChatGPT prompts with Claude and wondering why results feel inconsistent — you're not alone. Claude prompts work differently, and understanding why is the fastest path to dramatically better outputs. Anthropic's Claude is built with a distinct architecture and training philosophy that makes it respond better to certain types of instructions than any other AI model.

Claude processes instructions more literally than ChatGPT. It excels at long-context reasoning across documents up to 200,000 tokens. It follows negative constraints reliably (telling it what NOT to do actually sticks). And its structured reasoning capabilities make it exceptional for complex analysis, multi-step tasks, and careful document work.

This guide gives you 50 tested Claude prompts across writing, coding, analysis, research, and business — plus three full system prompt templates and specific formatting techniques that unlock Claude's full potential. Whether you're using Claude.ai, Claude API, or integrations like Cursor, these prompts will transform your results immediately.

Also worth reading: ChatGPT vs Claude prompts compared side-by-side and our deep dive on Claude claude-opus-4-6 prompts for advanced use cases.

Claude Prompt Formatting Tips

Before diving into the prompts, master these five techniques that are specific to Claude. They don't work as reliably in ChatGPT, but with Claude they consistently produce dramatically better outputs.

1. Use XML Tags for Structure

Claude was trained on structured data and responds exceptionally well to XML-style tags that define sections of your prompt. Wrap your instructions, documents, and context in descriptive tags:

<instructions>You are a senior financial analyst reviewing the following quarterly report...</instructions>
<document>[PASTE REPORT HERE]</document>
<task>Identify the three biggest risk factors and provide specific recommendations.</task>

This dramatically reduces ambiguity and helps Claude understand which parts of your prompt are context vs. instruction vs. content.

2. Leverage the 200K Context Window

Claude's 200,000-token context window (roughly 150,000 words) is one of its biggest advantages. Don't hold back — paste entire contracts, codebases, research papers, or meeting transcripts. Claude can synthesize and reason across all of it simultaneously, which is simply impossible in shorter-context models.

3. Be Explicit About Output Format

Claude follows format instructions very precisely. If you want bullet points, say "use bullet points." If you want a specific table structure, provide a template. If you want a word count, specify it. Unlike models that approximate format loosely, Claude will adhere closely to whatever structure you specify — even complex nested formats.

4. Chain-of-Thought Works Exceptionally Well

Adding "think step by step before answering" or "reason through this carefully before providing your final answer" unlocks Claude's strongest reasoning capabilities. For complex analysis, math, logic puzzles, or multi-variable decisions, chain-of-thought prompting with Claude produces notably better results than direct-answer prompting.

5. Negative Constraints Stick Better

Telling Claude what NOT to do is highly effective. Phrases like "do not include any caveats," "never use bullet points in this response," "avoid generic language," or "do not summarize what you're about to do — just do it" are reliably followed. This gives you fine-grained control over output quality and style that's harder to achieve with other models.

Writing Prompts for Claude

Claude's writing capabilities shine when given precise tone instructions and structural requirements. These 10 prompts are designed to leverage Claude's literal instruction-following for professional writing tasks.

1. Long-Form Content with Precise Tone

Write a [WORD COUNT]-word article on [TOPIC] for [TARGET AUDIENCE]. Tone: [TONE — e.g., authoritative but conversational, not academic]. Structure: compelling hook, 4-5 main sections with H2 headers, each with 2-3 supporting paragraphs. Do not use bullet points in the body. Do not add a summary section. End with a specific, actionable conclusion. No hedging language like "it's important to note" or "it's worth mentioning."

2. Essay with Steelmanned Counter-Arguments

Write a persuasive essay arguing [POSITION] on [TOPIC]. After making your strongest case in the first half, steelman the opposing view in the second half — present the best possible version of the counter-argument, then explain why your original position still holds. Total length: [WORD COUNT]. Academic but accessible tone. No conclusion paragraph that simply restates the thesis.

3. Detailed Product Description

Write a product description for [PRODUCT NAME], a [CATEGORY] designed for [TARGET CUSTOMER]. Features: [LIST FEATURES]. Primary benefit: [BENEFIT]. Tone: [BRAND VOICE]. Format: opening hook (1 sentence), aspirational paragraph (2-3 sentences), feature-benefit breakdown (each feature paired with a customer outcome), and a closing sentence with urgency. 200 words maximum. Avoid: "revolutionary," "innovative," "game-changing," "powerful."

4. Technical Documentation

You are a senior technical writer creating developer documentation. Write comprehensive documentation for [FUNCTION/API/FEATURE NAME]. Include: Overview (2-3 sentences), Parameters table (name, type, required, description), Return value, Code examples for basic and advanced usage, Error handling section, and Common pitfalls. Format for Markdown. Assume the reader is a mid-level developer familiar with [LANGUAGE/FRAMEWORK].

5. Research Summary for Non-Experts

Summarize the following research/report for a non-expert business audience. Strip out technical jargon — replace every piece of technical language with plain English equivalents. Structure: (1) What this research found in one sentence, (2) Why it matters for [INDUSTRY/CONTEXT], (3) Three specific implications for decision-makers, (4) What remains uncertain or contested. Maximum 400 words. [PASTE RESEARCH]

6. Persuasive Piece with Specific Rhetorical Techniques

Write a [LENGTH]-word persuasive piece arguing [POSITION]. Deliberately use these three rhetorical techniques: (1) logos — cite specific data or logical reasoning, (2) pathos — one concrete human story or scenario, (3) ethos — establish credibility through demonstrated expertise in [FIELD]. Label which technique you're using as a comment in brackets so I can see where each appears. Target reader: [AUDIENCE].

7. Style-Matched Rewrite

Analyze the writing style of the following sample text: [PASTE SAMPLE]. Identify: sentence length patterns, vocabulary register, use of first/second/third person, rhetorical devices, and structural preferences. Then rewrite [PASTE CONTENT TO REWRITE] in that exact style. After the rewrite, provide a brief note on the 3 most important style elements you matched.

8. Editorial Feedback Report

Provide editorial feedback on the following draft: [PASTE DRAFT]. Evaluate across five dimensions: (1) Argument clarity and logical flow, (2) Evidence quality and specificity, (3) Prose quality (sentence variety, word choice, pace), (4) Structural effectiveness, (5) Audience fit for [TARGET AUDIENCE]. For each dimension give a 1-10 score and specific, actionable suggestions. Identify the single biggest improvement opportunity.

9. Email Thread Analysis and Reply Draft

[PASTE FULL EMAIL THREAD] Analyze this email thread and: (1) Summarize the core issue in 2 sentences, (2) Identify any miscommunications, unstated assumptions, or tensions, (3) Draft a reply from [MY NAME/ROLE] that addresses all open questions, advances the situation constructively, and maintains a [TONE] tone. The reply should be under 150 words and not use corporate filler phrases.

10. Executive Brief from Long Document

[PASTE LONG DOCUMENT] Create a 1-page executive brief. Format: (1) Situation: what's happening and why it matters now, (2) Key findings: 3-5 bullet points with the most important data points, (3) Options: 2-3 courses of action with trade-offs, (4) Recommendation: one clear recommended action with rationale. Use direct, assertive language. No hedging. The reader is a C-level executive with 3 minutes to decide.

Coding Prompts for Claude

Claude's combination of strong reasoning and large context window makes it exceptional for coding tasks — especially reviewing, debugging, and understanding complex systems. These prompts are optimized for technical work.

11. Code Review with Security Focus

Perform a comprehensive code review on the following [LANGUAGE] code with an emphasis on security vulnerabilities. Check for: SQL injection, XSS, authentication/authorization flaws, insecure data exposure, dependency vulnerabilities, input validation gaps, and secrets in code. For each issue found: identify exact location, explain the risk, rate severity (Critical/High/Medium/Low), and provide corrected code. [PASTE CODE]

12. Refactoring for Readability and Maintainability

Refactor the following code to improve readability and long-term maintainability without changing behavior. Apply: meaningful variable and function names, single-responsibility principle, appropriate comments for non-obvious logic, removal of magic numbers/strings, and consistent style per [LANGUAGE] conventions. Provide the refactored version first, then a diff summary explaining each major change. [PASTE CODE]

13. Generate Comprehensive Unit Tests

Generate a complete unit test suite for the following function/class: [PASTE CODE]. Use [TEST FRAMEWORK — e.g., Jest, pytest, JUnit]. Include tests for: happy paths, edge cases, boundary values, invalid inputs, error/exception handling, and any async behavior. Each test should have a descriptive name that reads like documentation. Aim for 100% branch coverage. Add setup/teardown where appropriate.

14. Explain Architecture and Design Decisions

Analyze the following codebase/module and explain its architecture to a developer joining the team: [PASTE CODE]. Cover: overall design pattern being used, how the main components interact, data flow through the system, key abstractions and why they exist, potential pain points or technical debt, and what a developer should understand before making changes. Use diagrams described in plain text if helpful.

15. Debug with Reproduction Steps

I'm experiencing a bug in my [LANGUAGE/FRAMEWORK] application. Error: [ERROR MESSAGE]. Steps to reproduce: [LIST STEPS]. Expected behavior: [EXPECTED]. Actual behavior: [ACTUAL]. Here's the relevant code: [PASTE CODE]. Here's what I've already tried: [LIST ATTEMPTS]. Think through the possible causes systematically, starting with the most likely. For the top 3 candidates, explain the diagnosis and provide a specific fix for each.

16. Write Developer-Facing API Documentation

Write complete API documentation for the following endpoint(s): [PASTE CODE OR SPECIFICATION]. For each endpoint include: HTTP method and URL pattern, authentication requirements, request body schema (with field descriptions and validation rules), response schema (success and error cases with HTTP status codes), rate limiting notes, code examples in [LANGUAGES], and a cURL example. Format as Markdown suitable for a developer portal.

17. Generate an API Client

Generate a production-ready API client in [LANGUAGE] for the following API: [PASTE API SPEC OR DESCRIBE API]. The client should include: typed request/response models, error handling with custom exceptions, retry logic with exponential backoff, timeout configuration, authentication header injection, and a simple usage example. Follow [LANGUAGE] idioms and include docstrings/type hints.

18. SQL Query Optimization

Optimize the following SQL query for performance: [PASTE QUERY]. Database: [DATABASE TYPE AND VERSION]. Table sizes: [APPROXIMATE ROW COUNTS]. Current execution plan: [PASTE IF AVAILABLE]. Identify: bottlenecks in the current query, missing or suboptimal indexes, query restructuring opportunities, and any schema changes that would improve performance. Provide the optimized query with an explanation of each change and the expected performance impact.

19. Regex Generator with Explanation

Generate a regular expression to match [DESCRIBE PATTERN] in [LANGUAGE/FLAVOR]. Requirements: [LIST SPECIFIC REQUIREMENTS, e.g., must match X, must not match Y, handle edge cases Z]. Provide: (1) the regex pattern, (2) a step-by-step explanation of each component, (3) test cases showing what it matches and doesn't match, (4) any known limitations or edge cases to watch for. If multiple approaches exist, show the trade-offs.

20. System Design for Scale

Design a system to handle [USE CASE] at [SCALE — e.g., 100K users, 10M events/day]. Requirements: [LIST FUNCTIONAL AND NON-FUNCTIONAL REQUIREMENTS]. Think through: data model, API design, core services and their responsibilities, data storage choices with justification, caching strategy, queue/async processing, scalability bottlenecks, failure modes and resilience, and estimated infrastructure costs. Present as a structured technical design document with trade-off discussion.

Analysis Prompts for Claude

Claude's 200K context window and structured reasoning make it the best AI model for document-heavy analysis tasks. These prompts are designed for the kind of deep analytical work where other AI models fall short.

21. Financial Document Analysis

[PASTE FINANCIAL REPORT/10-K/EARNINGS CALL TRANSCRIPT] Analyze this financial document as a buy-side equity analyst. Extract: (1) Revenue drivers and growth quality (recurring vs one-time), (2) Margin trends and key cost items, (3) Balance sheet health and capital allocation, (4) Management commentary on risks and guidance, (5) Red flags or items requiring deeper investigation. Format as a structured analyst note. Quantify every claim with specific numbers from the document.

22. Competitive Analysis from Raw Data

I'm providing data on [COMPANY/PRODUCT] and its main competitors: [PASTE DATA — pricing, features, reviews, public financials, etc.]. Analyze this competitive landscape and produce: (1) Feature comparison matrix, (2) Positioning map description, (3) Pricing strategy analysis, (4) Each competitor's apparent strategic priority, (5) Our clearest competitive advantages and gaps, (6) Three strategic recommendations based on the data. Be specific — cite data, not generalizations.

23. Survey Result Synthesis

Analyze the following survey results from [N] respondents about [TOPIC]: [PASTE DATA]. Identify: (1) Key themes in quantitative responses with statistical significance notes, (2) Sentiment patterns in open-ended responses (group by theme, not individual), (3) Segments showing meaningfully different responses (by [DEMOGRAPHIC VARIABLES]), (4) Surprising or counter-intuitive findings, (5) Actionable recommendations for [DECISION-MAKER]. Distinguish clearly between what the data shows vs. what it suggests.

24. Contract Clause Extractor

[PASTE CONTRACT TEXT] You are a commercial attorney reviewing this contract for [PARTY NAME] as the [ROLE — e.g., buyer, licensee]. Extract and summarize all clauses related to: (1) Payment terms and penalties, (2) Termination rights and conditions, (3) Liability limitations and indemnification, (4) Intellectual property ownership, (5) Non-compete and exclusivity provisions, (6) Dispute resolution. For each clause: quote the relevant text, explain it in plain English, and flag any terms that are non-standard or potentially risky for our position.

25. Legal Brief Summary

[PASTE LEGAL BRIEF OR COURT DOCUMENT] Summarize this legal document for a business executive (non-lawyer). Provide: (1) The core legal issue in one paragraph, (2) The parties' positions in plain English, (3) Key legal arguments made by each side, (4) What outcome is being sought, (5) Business implications if each side prevails, (6) Key dates or deadlines. Use plain language throughout. Flag any immediate action items.

26. Multi-Document Comparison

[PASTE DOCUMENT 1] [PASTE DOCUMENT 2] Compare these two documents on [TOPIC]. Identify: (1) Points of agreement, (2) Points of direct contradiction or tension, (3) Claims in Document 1 not addressed in Document 2 and vice versa, (4) Differences in underlying assumptions or methodology, (5) Which document's analysis is better supported by evidence and why. Present as a structured comparison, not a running narrative.

27. Data Anomaly Detection

Examine the following dataset and identify anomalies, outliers, and data quality issues: [PASTE DATA]. For each anomaly: (1) Identify the specific data point(s) or pattern, (2) Quantify how far it deviates from expected patterns, (3) Suggest whether it represents a genuine outlier, data entry error, or system issue, (4) Recommend how to handle it for analysis purposes. Provide a data quality summary at the end with an overall quality score and the three most critical issues.

28. Root Cause Analysis

Perform a structured root cause analysis for the following problem: [DESCRIBE PROBLEM WITH SYMPTOMS AND TIMELINE]. Apply the 5 Whys method: start from the observable symptom and systematically ask why until you reach a root cause. Then validate using fishbone analysis — examine potential causes across: People, Process, Technology, Environment, and Materials. Provide: confirmed root cause, contributing factors, short-term containment action, and long-term corrective action.

29. Stakeholder Map from Situation Description

Based on the following project/situation description, create a comprehensive stakeholder map: [DESCRIBE PROJECT OR SITUATION]. For each stakeholder group: (1) Name and role, (2) Level of influence (High/Medium/Low), (3) Level of interest (High/Medium/Low), (4) Current position (Supporter/Neutral/Resistant), (5) Their primary concern or motivation, (6) Recommended engagement strategy. Present as a structured table, then provide a prioritized engagement plan for the top 5 stakeholders.

30. SWOT Analysis from Meeting Transcript

[PASTE MEETING TRANSCRIPT OR NOTES] Extract a comprehensive SWOT analysis from this transcript. Attribute each point to specific statements made in the transcript (quote briefly). Go beyond the obvious — identify implicit strengths, weaknesses, opportunities, and threats that participants may have alluded to without naming directly. After the SWOT, create a 2x2 strategy matrix: SO strategies (strengths + opportunities), ST strategies (strengths + threats), WO strategies (weaknesses + opportunities), WT strategies (weaknesses + threats).

Research Prompts for Claude

Claude's ability to synthesize across large documents, maintain logical consistency, and reason carefully about evidence quality makes it an exceptional research partner. These prompts are for researchers, analysts, and knowledge workers.

31. Deep Literature Review

Act as a research librarian and academic writing specialist. Based on the following sources I've compiled on [TOPIC]: [PASTE SOURCES OR KEY FINDINGS]. Synthesize a structured literature review covering: (1) Evolution of thinking on this topic over time, (2) Major schools of thought and their key proponents, (3) Points of scholarly consensus, (4) Active debates and contested areas, (5) Methodological trends and limitations across the field. Academic tone, third-person, 600-800 words. Flag any areas where I need stronger sourcing.

32. Fact-Check an Argument

[PASTE ARGUMENT OR CLAIM SET] Fact-check this argument systematically. For each factual claim: (1) Assess whether it is verifiable, (2) Identify what type of evidence would confirm or refute it, (3) Note whether the claim is a fact, inference, or opinion being stated as fact, (4) Flag any logical fallacies in how evidence is used. Provide an overall credibility assessment and identify the single weakest link in the argument's evidential chain.

33. Counter-Argument Generator

I am arguing that [POSITION]. Generate the five strongest possible counter-arguments to my position. For each counter-argument: (1) State it clearly and charitably (steelman it — make it as strong as possible), (2) Identify what type of objection it is (empirical, logical, ethical, practical), (3) Rate its strength as a counter-argument (1-10), (4) Suggest what evidence or reasoning I would need to rebut it. Do not argue for my position — your job is to identify its genuine vulnerabilities.

34. Evidence Hierarchy Builder

I'm building an argument for [CLAIM/POSITION]. Here is the evidence I've collected: [LIST EVIDENCE]. Organize this evidence into an evidence hierarchy: (1) Strongest evidence — directly supports the claim with high certainty, (2) Supporting evidence — corroborates the claim but with caveats, (3) Weak or circumstantial evidence — suggestive but not conclusive, (4) Evidence that needs verification before use, (5) Evidence that actually undermines my position (be honest). Recommend which evidence to lead with and which to avoid.

35. Trend Analysis from Multiple Sources

[PASTE 3-5 ARTICLES, REPORTS, OR DATA SETS] Identify the key trends across these sources about [TOPIC]. For each trend: (1) Name and describe it, (2) Show how multiple sources corroborate it (or where they diverge), (3) Assess whether it is accelerating, stabilizing, or reversing based on the evidence, (4) Rate the confidence level in this trend (High/Medium/Low) and explain why. Conclude with a 2-year outlook based on the trend trajectories you've identified.

36. Thesis Strengthener

Review the following thesis statement and argument structure: [PASTE THESIS AND OUTLINE]. Provide: (1) Assessment of the thesis — is it arguable, specific, and significant? (2) Logical gap analysis — where does the argument chain break down or make unsupported leaps? (3) Three ways to strengthen the thesis statement itself, (4) Two additional argument pillars that would make the overall case more robust, (5) The most vulnerable point in the current structure and how to address it.

37. Expert Interview Question Generator

I'm interviewing [EXPERT NAME/ROLE] about [TOPIC] for [PURPOSE — e.g., research paper, podcast, article]. What I already know about their work/perspective: [CONTEXT]. Generate 15 interview questions designed to: surface novel insights beyond their public statements, probe assumptions in their work, explore practical implications, invite them to disagree with conventional wisdom, and reveal what they find most uncertain or unresolved. Avoid yes/no questions. Order from rapport-building to deeper probing.

38. Bibliography from Topic Description

I'm researching [TOPIC] for [PURPOSE]. Generate a structured research bibliography that includes: (1) Foundational/classic works I should read first, (2) Current leading researchers and their key recent publications, (3) Best primary data sources (databases, government sources, institutional research), (4) Most cited review articles on this topic, (5) Productive adjacent fields that could offer cross-disciplinary insights. For each entry provide: why it's valuable and what type of reader it's best suited for. Note: I will verify all citations independently.

39. Research Gap Finder

[PASTE LITERATURE REVIEW OR SUMMARY OF EXISTING RESEARCH] Identify unexplored or under-explored research gaps in this field. For each gap: (1) Describe what's missing from the current literature, (2) Explain why this gap matters for theory or practice, (3) Suggest what type of study could fill it (methodology, data needed), (4) Rate the feasibility of investigating this gap (High/Medium/Low) with explanation. Identify the single most significant and feasible gap that a researcher should prioritize.

40. Academic Abstract Writer

[PASTE FULL PAPER OR DETAILED OUTLINE] Write a 250-word academic abstract following the IMRaD structure: (1) Background/motivation — why this study was needed, (2) Methods — what was done and how, (3) Results — key quantitative findings, (4) Discussion/conclusion — what the findings mean and their implications. Use precise, hedged academic language. Do not include information not present in the paper. Make the first sentence compelling enough to make a researcher want to read the full paper.

Business Prompts for Claude

These business prompts leverage Claude's ability to handle complex, multi-stakeholder documents and produce executive-quality deliverables. Each is designed for senior business contexts where precision and clarity matter.

41. Board Deck Summary

[PASTE BOARD PRESENTATION CONTENT OR NOTES] Prepare the executive summary section for this board deck. Include: (1) Business situation in 3 sentences, (2) The 3 most critical decisions or approvals needed from the board today, (3) Performance vs. prior commitments (what we said, what happened), (4) Key risks requiring board awareness, (5) Priorities for the next quarter. Write for a board audience — sophisticated, time-limited readers who expect directness. No slides description — prose format, under 500 words.

42. Investor Q&A Preparation

I'm preparing for a meeting with [TYPE OF INVESTOR — e.g., Series B VC, strategic corporate investor] to discuss [COMPANY STAGE AND TOPIC]. Our current metrics: [KEY METRICS]. Known concerns investors have raised: [LIST CONCERNS]. Generate the 15 hardest questions they are likely to ask, with suggested answers for each. For each Q&A: flag if our answer is weak and needs better data or framing, and note what follow-up question my answer is likely to invite. Be brutally honest about our vulnerabilities.

43. Hiring Criteria Developer

Help me develop clear, unbiased hiring criteria for a [JOB TITLE] role at [COMPANY TYPE/STAGE]. The role is responsible for: [KEY RESPONSIBILITIES]. Team context: [TEAM SIZE AND COMPOSITION]. Success in 90 days looks like: [OUTCOMES]. Generate: (1) Must-have qualifications (evidence-based, not credential-based), (2) Nice-to-have qualifications, (3) Disqualifying signals, (4) 8 structured interview questions with what good answers look like, (5) A rubric for evaluating candidates consistently. Avoid criteria that inadvertently screen out underrepresented candidates.

44. Policy Draft Reviewer

[PASTE DRAFT POLICY DOCUMENT] Review this draft policy document. Evaluate: (1) Clarity — are instructions unambiguous and actionable? (2) Coverage — what scenarios does it fail to address? (3) Consistency — does it contradict other common policies or norms? (4) Enforceability — can compliance realistically be verified? (5) Fairness — does it treat all affected parties equitably? For each issue, quote the specific language and provide a revised version. Prioritize issues by impact severity. Also flag any legal or compliance red flags.

45. Meeting Action Items Extractor

[PASTE MEETING TRANSCRIPT OR NOTES] Extract all action items from this meeting. For each action item: (1) What needs to be done (specific, not vague), (2) Who owns it (person named or implied), (3) Deadline (stated or implied), (4) Any dependencies or blockers mentioned. Also extract: (5) Decisions made in the meeting, (6) Open questions that still need resolution, (7) Any commitments made that weren't framed as action items but should be tracked. Format as a clean follow-up document I can send to attendees.

46. NPS Response Classifier

Classify and analyze the following NPS survey responses: [PASTE RESPONSES WITH SCORES]. For each response: assign to a category (e.g., Product quality, Customer service, Price/value, Ease of use, Feature request, Competitor mention). For the full dataset: (1) Identify the top 5 themes by frequency and their average NPS score, (2) Surface the 3 most actionable insights, (3) Flag any critical issues mentioned by Promoters (score 9-10) — these are highly valuable signals, (4) Compare Detractor (0-6) vs Promoter themes to identify key differentiation gaps.

47. Product Spec from User Stories

[PASTE USER STORIES OR FEATURE REQUESTS] Transform these user stories into a structured product specification. Include: (1) Problem statement synthesized from user needs, (2) Solution overview and key design principles, (3) Functional requirements (organized by user journey stage), (4) Non-functional requirements (performance, security, accessibility), (5) Out-of-scope items, (6) Success metrics — how will we know this feature is working? (7) Open questions for the team to resolve. Format for engineering handoff.

48. OKR Developer

Help me develop OKRs for [TEAM/DEPARTMENT] for [TIME PERIOD]. Our company's top priority this period is [COMPANY GOAL]. Our team's main responsibilities: [LIST]. Known challenges: [LIST]. Generate: 3 Objectives (ambitious, qualitative, inspiring) each with 3-4 Key Results (specific, measurable, time-bound, 70% stretch). For each KR, specify: the measurement method, baseline (where we are now), and target. Flag any KRs that are outputs (things we do) rather than outcomes (results we achieve) — outcomes are preferred.

49. Strategic Memo

Write a strategic memo recommending [RECOMMENDATION] to [AUDIENCE — e.g., CEO, leadership team]. Context: [SITUATION DESCRIPTION]. Format: (1) Recommendation (1 sentence — lead with it), (2) Situation (why action is needed now), (3) Analysis (3 key data points or observations supporting the recommendation), (4) Options considered (2-3 alternatives with brief trade-offs), (5) Recommendation rationale (why this option over others), (6) Required actions and timeline. One page maximum. Direct, assertive tone. No hedge words.

50. Risk Register Builder

Create a comprehensive risk register for [PROJECT/INITIATIVE]. Context: [DESCRIPTION]. For each risk: (1) Risk name and description, (2) Risk category (Strategic/Operational/Financial/Compliance/Reputational/Technical), (3) Likelihood (1-5), (4) Impact (1-5), (5) Risk score (Likelihood × Impact), (6) Current mitigations in place, (7) Residual risk score after mitigations, (8) Owner, (9) Early warning signals to monitor. Sort by risk score descending. Identify the three risks requiring immediate escalation and explain why.

Claude System Prompt Templates

System prompts define Claude's persona, constraints, and behavior across an entire conversation or application. These three production-ready templates cover the most common use cases. Paste them into the "System" field in Claude.ai or via the API's system parameter.

Customer Support Agent System Prompt

You are a customer support specialist for [COMPANY NAME], a [DESCRIPTION OF BUSINESS]. Your role is to help customers resolve issues quickly, professionally, and empathetically. KNOWLEDGE BASE: [PASTE RELEVANT PRODUCT/POLICY INFORMATION] GUIDELINES: - Greet the customer warmly and acknowledge their issue before problem-solving - Ask one clarifying question at a time — never ask multiple questions in one message - Always offer at least one concrete next step, even if the full resolution requires escalation - If you cannot resolve an issue, say clearly what you can do and who can help further - Never promise outcomes you cannot guarantee - Apologize for genuine problems without excessive self-flagellation - Tone: warm, professional, and efficient — not scripted or robotic ESCALATION TRIGGERS: Escalate to a human agent (say "I'll connect you with a specialist now") if: the customer expresses anger or threatens legal action, the issue involves a billing dispute over $[AMOUNT], the problem has occurred more than twice, or you cannot resolve the issue within 3 messages. NEVER: - Discuss competitor products negatively - Share internal processes or non-public information - Make commitments about future features or pricing changes - Use hollow phrases like "Great question!" or "I understand your frustration" as openers

Research Assistant System Prompt

You are an expert research assistant with a background in [FIELD/DOMAIN]. You help researchers, analysts, and knowledge workers find, synthesize, and analyze information effectively. YOUR APPROACH: - Always distinguish clearly between facts, inferences, and opinions — label them explicitly when it matters - When synthesizing multiple sources, note where they agree and where they diverge - Proactively flag when a question is contested in the field or when evidence is limited - Suggest follow-up questions and adjacent research directions the user might not have considered - Use precise academic language when accuracy is critical; simplify when clarity serves better SOURCE STANDARDS: - Prefer peer-reviewed research, primary data sources, and expert consensus over secondary commentary - Always note the limitations of sources you reference (date, sample size, methodology, potential bias) - When asked for citations, be transparent that you cannot guarantee accuracy — recommend verification OUTPUT FORMAT: Unless instructed otherwise: use headers for multi-section responses, number key claims for easy reference, and conclude with "Key uncertainties" and "Suggested next steps" sections. CONTEXT ABOUT MY WORK: [PASTE RELEVANT BACKGROUND ON THE USER'S RESEARCH PROJECT OR DOMAIN]

Code Reviewer System Prompt

You are a senior software engineer performing code reviews. Your goal is to improve code quality, catch bugs, and help developers grow — not to criticize or nitpick. REVIEW PRIORITIES (in order): 1. Correctness — does this code do what it's supposed to do? 2. Security — are there any vulnerabilities or unsafe patterns? 3. Performance — are there obvious inefficiencies at scale? 4. Maintainability — will the next developer understand and safely modify this? 5. Style — does it follow team conventions? (lowest priority) REVIEW STYLE: - Lead with what works well before describing issues - For every problem, suggest a specific fix — not just "this is problematic" - Categorize feedback: [MUST FIX], [SHOULD FIX], [CONSIDER], [NIT] - Explain the "why" behind every [MUST FIX] — help the developer learn, not just comply - Rate overall code quality: Approved / Approved with suggestions / Request changes CONTEXT: Language: [LANGUAGE] Framework: [FRAMEWORK] Project type: [TYPE] Team conventions: [DESCRIBE OR PASTE STYLE GUIDE]

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The full library includes 500+ tested prompts for Claude, ChatGPT, Gemini, and Midjourney across every professional use case.

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Frequently Asked Questions

Are Claude prompts different from ChatGPT prompts?

Yes — Claude and ChatGPT respond differently to the same prompts. Claude is more literal in following instructions, better at respecting negative constraints (what NOT to do), and excels with long structured inputs using XML tags. Claude also has a much larger context window (200K tokens) which changes what's possible in a single prompt. Prompts that work well for ChatGPT often need adjustment for Claude — particularly around format specification and structure.

What is Claude best at?

Claude excels at: long-document analysis (reading entire contracts, codebases, or reports), nuanced reasoning on complex multi-variable problems, careful adherence to tone and format instructions, code review and explanation, and tasks requiring precise instruction-following. Its 200K context window makes it uniquely suited to processing book-length content in a single prompt — something no other mainstream AI model matches at the same quality level.

How do I get Claude to follow format instructions?

The most reliable technique is XML tags — wrap instructions in <instructions> tags and content in <document> or <content> tags. You can also include a literal example of the format you want, or end your prompt with "Respond only in the format below:" followed by a template. Claude also responds well to explicit negative constraints: "do not use bullet points," "do not include a summary paragraph." For complex formats, provide a complete template rather than describing it abstractly.

What is Claude's context window?

Claude 3.5 Sonnet, Claude 3 Opus, and newer models support a 200,000-token context window — approximately 150,000 words or about 500 pages of dense text. This is the largest context window of any major AI model and is one of Claude's most significant practical advantages. You can paste entire books, large codebases, or lengthy document sets and Claude can reason across all of it in a single session.

Next Steps with Claude Prompts

The 50 prompts in this guide cover the core professional use cases where Claude consistently outperforms alternatives. Start with the category most relevant to your daily work — copy a prompt, fill in your specifics, and test it. Pay attention to how Claude responds to format instructions and XML tags; once you see the difference, you'll want to restructure all your prompts.

For the most demanding tasks — processing entire books, reviewing large codebases, or analyzing hundreds of pages of documents — Claude's context window advantage is irreplaceable. Explore advanced Claude claude-opus-4-6 prompts for high-stakes research and analysis work.

For a direct comparison of when to use Claude vs. ChatGPT for specific tasks, see our side-by-side prompt comparison guide. And for mastering the underlying principles behind all great prompts, our prompt engineering guide covers the frameworks that work across every AI model.