🔥 The Core Problem
"DeepSeek is powerful, but why does my weekly report read like a robot’s diary?"
"Others create genius plans with AI—mine keeps derailing!"
Key Insight: DeepSeek, as a reasoning-focused LLM, thrives on guided thinking, not blunt commands. Think of prompts as your "AI camera lens"—poor focus = blurry results.
🛠️ 4 Techniques to Unlock DeepSeek’s Full Potential
1. The "Goal-Scenario-Pain" Framework
Formula: What? → Who? → Why?
- Bad Prompt: "Write a product description." → Confuses anyone.
- Smart Prompt:textCopy Code
"Create a SaaS tool intro for busy HR managers (Goal) – Highlight 1-click payroll automation (Scenario) – Address data security concerns upfront (Pain)"
Why It Works: DeepSeek’s logic engine prioritizes contextual reasoning over generic outputs.
2. Humanize Complexity Principle
Ideal for: Training docs, client communications,科普
- Jargon Alert:
"Utilize stochastic gradient descent to optimize loss functions." → ❌ - Life Metaphor:
"Like learning to bike:
AI adjusts its 'balance' (parameters) after each 'fall' (error)
until it rides smoothly (optimal model)." → ✅
Pro Tip: Add "Explain this to a 10-year-old" to prompts for instant simplification.
3. Reverse Interrogation Method
When outputs feel incomplete:
- Critique Mode:
"As a user, what 3 flaws would you criticize in this proposal?" - Multi-Angle Refinement:
"Re-optimize this 618 campaign plan through cost, feasibility, and innovation lenses."
Case Study: After generating an event plan, ask:
"If you’re a cash-strapped startup CEO, which 2 budget items would you cut? Why?"
→ Result: AI suggested scrapping offline promotions and added viral红包+community challenges.
4. Style Cloning via "Template Feeding"
3-Step Process:
- Provide a sample document (e.g., a stellar report)
- Define direction: "Adopt this structure but focus on Q3 metrics."
- Demand output: "Generate a risk analysis in this format."
2025 Data: Users applying this method achieve 68% faster onboarding with 92% style consistency.
🚀 Why Prompt Skills = Career Capital
The AI landscape shows two seismic shifts:
- Tool Democratization: Everyone has AI—winners are those who orchestrate it best
- Prompt Engineering Gold Rush: Top prompt designers earn $220k+ annually (LinkedIn 2025 Report)
Real-World Impact:
| "Write a product post" | "Draft a viral TikTok script for Gen Z gamers highlighting battery life vs ASUS ROG 2025" | B’s CTR ➔ 3x Higher |
📅 Action Plan for 2025
- Build Your Prompt Library: Template needs (reports, emails, proposals)
- AI Debrief Ritual: After each output, ask: "What are 3 weaknesses here?" → Trains critical thinking
- Join DeepSeek’s Prompt Hub: Access 500+ validated templates (Free until 3/1/2025)
👉 Start Now: Your next prompt could be worth $1,000/hour in saved productivity.
I. Why Does Your DeepSeek Always Seem “Ineffective”?
"Everyone says DeepSeek is powerful, but why does it turn my weekly report into a mundane diary when I ask it to write one?" "Others can write proposals in one go with AI, but mine always goes off track." Recently, I've received numerous complaints from readers, but the crux of the problem lies not in the AI, but in the communication method. It's like taking photos with a phone: some people can capture stunning shots effortlessly, while others can only press the shutter button. AI prompts are the "focusing tool" between you and the AI. As an observer deeply involved in the AI field, I've found that DeepSeek, as a reasoning-based large model, requires "guided thinking" rather than "direct commands." Today, I'll share four practical techniques, combined with real-life workplace examples, to help you fully unleash the potential of AI!
II. Four Techniques to Make AI Instantly Understand Your Needs
Technique 1: Use the "Goal-Scenario-Pain Point" Framework to Precisely Lock Down Requirements
Formula: What to do? For whom? What to fear?
Instead of hesitating between "Kimi, Zhipu Qingyan, Tongyi Qianwen, ERNIE Bot, Doubao, Tiangong AI, Tencent Yuanbao," wondering which to choose, it's better to learn how to use DeepSeek! - JieYingAI (AI: ???)
Principle: DeepSeek excels in logical reasoning. Clarifying the goal, usage scenario, and potential problems can help the AI quickly orient its creative direction. Besides, simply saying "help me write a product introduction" would confuse anyone.
Technique 2: Make AI "Speak Human Language," Turning Complex Concepts into Colloquial Language
Applicable Scenarios: Popular science articles, training materials, external communication
Instead of being puzzled by jargon, try changing the way you ask questions.
AI Answer Example: "Imagine when you first rode a bike, you always swayed left and right. Each time you fell, your brain would remember to 'push left when the handlebars tilt right.' AI learning is the same—it constantly calculates errors (falls), adjusts parameters in its 'brain' (algorithm), and eventually finds a balance."
Value: Using everyday language lowers the barrier to understanding, especially suitable for workplace professionals who need to communicate externally.
Technique 3: Reverse Questioning to Elicit AI's "Hidden Skills"
When you feel the answer isn't perfect, try these two steps:
Critical Inquiry: "If you were a user, what three points would you criticize about this proposal?"
Multi-Angle Review: "Please reoptimize the proposal from the dimensions of cost, feasibility, and innovation."
Case Study: After an operations personnel generated a "618 event plan," they added a question: "Assuming you are the boss of a startup with a tight budget, which two expenses in the plan would you cut? Why?"
Result: The AI not only reduced offline promotion costs but also added a low-cost customer acquisition strategy of "fission red packets + community check-ins."
Technique 4: Feed "Sample Essays" to Make AI Your Style Cloner
Three-Step Operation: Provide an Example → Define Direction → Request Result
Advantage: Quickly reuse high-quality content frameworks, allowing beginners to produce "expert-level" reports.
III. In the AI Era, Those Who Know How to Ask Questions Have Already Pulled Ahead
The AI industry is currently undergoing two major transformations:
Tool Democratization: The technical barrier has lowered, and the competition is now about "who can better harness AI."
Prompt Professionalization: The job title of "Prompt Engineer" has emerged overseas, with annual salaries reaching millions.
Your prompt writing skills directly determine whether the AI is an "intern" or an "expert consultant."
Example: Two new media operators using the same AI tool—the results are obvious: User B's copy click-through rate is three times that of User A.
IV. Action Guide: Start Changing Today
Build Your "Prompt Library"
Organize high-frequency needs (weekly reports, emails, proposals) into templates and iteratively optimize them.
Use the "AI Review Method" to Enhance Thinking
After each content generation, ask the AI: "What are the three deficiencies of this answer?" This reverses the training of your logical abilities.
Research
DeepSeek-V3 Capabilities
DeepSeek-V3 achieves a significant breakthrough in inference speed over previous models.
It tops the leaderboard among open-source models and rivals the most advanced closed-source models globally.
| Benchmark (Metric) | DeepSeek V3 | DeepSeek V2.5 | Qwen2.5 | Llama3.1 | Claude-3.5 | GPT-4o | |
|---|---|---|---|---|---|---|---|
| 0905 | 72B-Inst | 405B-Inst | Sonnet-1022 | 0513 | |||
| Architecture | MoE | MoE | Dense | Dense | - | - | |
| # Activated Params | 37B | 21B | 72B | 405B | - | - | |
| # Total Params | 671B | 236B | 72B | 405B | - | - | |
| English | MMLU (EM) | 88.5 | 80.6 | 85.3 | 88.6 | 88.3 | 87.2 |
| MMLU-Redux (EM) | 89.1 | 80.3 | 85.6 | 86.2 | 88.9 | 88.0 | |
| MMLU-Pro (EM) | 75.9 | 66.2 | 71.6 | 73.3 | 78.0 | 72.6 | |
| DROP (3-shot F1) | 91.6 | 87.8 | 76.7 | 88.7 | 88.3 | 83.7 | |
| IF-Eval (Prompt Strict) | 86.1 | 80.6 | 84.1 | 86.0 | 86.5 | 84.3 | |
| GPQA-Diamond (Pass@1) | 59.1 | 41.3 | 49.0 | 51.1 | 65.0 | 49.9 | |
| SimpleQA (Correct) | 24.9 | 10.2 | 9.1 | 17.1 | 28.4 | 38.2 | |
| FRAMES (Acc.) | 73.3 | 65.4 | 69.8 | 70.0 | 72.5 | 80.5 | |
| LongBench v2 (Acc.) | 48.7 | 35.4 | 39.4 | 36.1 | 41.0 | 48.1 | |
| Code | HumanEval-Mul (Pass@1) | 82.6 | 77.4 | 77.3 | 77.2 | 81.7 | 80.5 |
| LiveCodeBench (Pass@1-COT) | 40.5 | 29.2 | 31.1 | 28.4 | 36.3 | 33.4 | |
| LiveCodeBench (Pass@1) | 37.6 | 28.4 | 28.7 | 30.1 | 32.8 | 34.2 | |
| Codeforces (Percentile) | 51.6 | 35.6 | 24.8 | 25.3 | 20.3 | 23.6 | |
| SWE Verified (Resolved) | 42.0 | 22.6 | 23.8 | 24.5 | 50.8 | 38.8 | |
| Aider-Edit (Acc.) | 79.7 | 71.6 | 65.4 | 63.9 | 84.2 | 72.9 | |
| Aider-Polyglot (Acc.) | 49.6 | 18.2 | 7.6 | 5.8 | 45.3 | 16.0 | |
| Math | AIME 2024 (Pass@1) | 39.2 | 16.7 | 23.3 | 23.3 | 16.0 | 9.3 |
| MATH-500 (EM) | 90.2 | 74.7 | 80.0 | 73.8 | 78.3 | 74.6 | |
| CNMO 2024 (Pass@1) | 43.2 | 10.8 | 15.9 | 6.8 | 13.1 | 10.8 | |
| Chinese | CLUEWSC (EM) | 90.9 | 90.4 | 91.4 | 84.7 | 85.4 | 87.9 |
| C-Eval (EM) | 86.5 | 79.5 | 86.1 | 61.5 | 76.7 | 76.0 | |
| C-SimpleQA (Correct) | 64.1 | 54.1 | 48.4 | 50.4 | 51.3 | 59.3 |
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