DeepSeek Prompt Generator — Build Expert Prompts with PromptBro
PromptBro builds structured prompts for DeepSeek R1 and DeepSeek V3 — optimised for complex reasoning, hard coding challenges, mathematical analysis, and the multi-step technical tasks where DeepSeek genuinely earns its reputation.
What Makes a Great DeepSeek Prompt?
DeepSeek R1 is a reasoning model — it works best when you give it hard, multi-step problems that require working through logic rather than retrieving facts. Unlike conversational models, R1 shows its chain-of-thought reasoning transparently in its response (the think blocks), so you can see exactly how it approaches a problem before it commits to an answer. This transparency is a genuine advantage for debugging reasoning errors. R1 excels at mathematics, competitive programming problems, logical proofs, and architectural decisions — tasks where the quality of the reasoning process matters as much as the final answer.
DeepSeek V3 is the conversational/instruction-following model — strong at coding, analysis, and structured tasks, and a cost-effective alternative to GPT-4o and Claude for high-volume use cases. When prompting R1, give it genuinely difficult problems — it underperforms on easy tasks that don't require extended reasoning. Explicitly ask it to reason through steps before giving a final answer for best results. For coding tasks, include test cases or acceptance criteria in your prompt; DeepSeek will write code that satisfies them rather than assuming what you mean. The model handles English and Chinese equally well.
Example DeepSeek Prompts
Complex reasoning problem — R1
Reason through this problem step by step before giving your final answer. A startup has three potential go-to-market strategies for entering the European B2B SaaS market: Strategy A: Direct sales — hire 3 enterprise AEs in Germany, UK, and Netherlands. Cost: €800k/year. Expected ARR after 18 months: €600k–€1.2M. Strategy B: Channel partnerships — integrate with 5 established CRMs and rely on their partner programs. Cost: €200k/year in integration + rev share. Expected ARR after 18 months: €300k–€900k. Strategy C: Product-led growth — freemium tier, €29/month paid plan, self-serve. Cost: €350k/year in product + marketing. Expected ARR after 18 months: €200k–€1.5M. The company has €1.5M runway. They need to reach profitability within 24 months or raise a Series A. Analyse which strategy is optimal, considering: capital efficiency, risk distribution, strategic optionality, and what each strategy signals to future investors. Make a definitive recommendation with clear reasoning.
Coding challenge — with test cases
Write a solution to the following problem. Think through your approach before writing any code. Problem: Implement a rate limiter class that supports multiple users with individual rate limits. Requirements: - Each user has a configurable max requests per time window (e.g., 100 requests per 60 seconds) - The limiter must use a sliding window algorithm (not fixed window) - Must be thread-safe - Memory usage must be O(max_requests) per user, not O(all_time_requests) - Should support millions of concurrent users efficiently Test cases your solution must pass: 1. User with 3 req/10s limit: requests at t=0,1,2 → all allowed; request at t=3 → denied; request at t=11 → allowed 2. Two users with different limits should not interfere with each other 3. Concurrent requests from the same user must not cause race conditions Language: Python. Include type hints. Include a brief explanation of your algorithmic choice and why you rejected simpler approaches.
Mathematical analysis — proof and application
Work through this carefully, showing all reasoning. Problem: A company's user growth follows a logistic model: P(t) = K / (1 + ((K - P₀) / P₀) * e^(-rt)) Where: K = 500,000 (market saturation), P₀ = 1,000 (current users), r = unknown growth rate, t = months Part 1: If the company reaches 50,000 users in 18 months, what is r? Show the derivation. Part 2: Using that r, calculate: - When they'll reach 250,000 users (50% of K) - The rate of growth (dP/dt) at the inflection point - The month when growth rate begins to decelerate Part 3: The company is considering two strategies — one that doubles r but reduces K to 300,000 (tighter niche), another that keeps r but increases K to 800,000 (broader market). Which maximises total users at month 36? Show the comparison numerically. Verify each calculation and flag any assumptions you're making.
Technical architecture review
Review the following system architecture and identify the failure modes, scaling bottlenecks, and architectural risks. Think through each component before writing your assessment. System: Real-time collaborative document editor (like Google Docs) Current architecture: - Frontend: React SPA, WebSocket connection per user per document - WebSocket server: Single Node.js process, in-memory state for active documents - Database: PostgreSQL, document snapshots saved every 30 seconds - CDN: Static assets only - Auth: JWT tokens, 24-hour expiry Scale target: 100,000 concurrent users, documents up to 500 active collaborators For each component, tell me: 1. What breaks first and at what approximate scale 2. The root cause (not just "it won't scale" — explain the specific mechanism) 3. The standard solution and its tradeoffs 4. Any non-obvious risks that aren't immediately apparent End with a prioritised refactor roadmap: what must change before launch, what can wait until you hit 10k users, what can wait until 100k.
Build your own DeepSeek prompt with PromptBro
PromptBro's 6-step guided flow builds prompts optimised for DeepSeek R1 and V3 — structured for complex reasoning, explicit step-by-step thinking, coding challenges with acceptance criteria, and the technical depth where DeepSeek outperforms alternatives.
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