Featured3 Principles of Deepseek Prompts for High-Quality Results

Recent intensive research into Deepseek's principles, applications, and practices has revealed key insights into its effective utilization. The optimal use of this AI tool fundamentally depends on understanding prompt engineering principles to achieve maximum effectiveness. The findings presented here illuminate critical aspects of prompt construction and implementation. As a sophisticated generative AI system, Deepseek demonstrates remarkable capabilities across various applications, including translation, dialogue generation, logical reasoning, and content completion.

What are the three key principles that should be followed when using prompts to achieve the best results with AI assistants like Deepseek? Clarity, Structure, and Detailed.

Why? Fundamentally, it is about making our prompts fit the cognitive model of the AI. The core technologies of AI assistants like Deepseek are: Attention mechanism, Knowledge retrieval path, and Information generation confidence control. The three principles of prompts correspond to these three key technologies. AI systems are designed to process these prompts effectively, ensuring accurate and contextually relevant responses.

First: Clarity in Writing Effective AI Prompts

“Attention Is All You Need” - the attention mechanism is the most important and core algorithmic mechanism in modern AI, and the AI model must be allowed to focus all its attention on a clear target, often achieved through a well-crafted AI prompt. If the target is not clear, the AI will try to explore, and the attention will be scattered. If the target is ambiguous, DeepSeek is likely to fall into the trap of natural language polysemy, and the attention will be scattered. If there are multiple targets, the attention will be scattered: this is easy to understand - if the 20-second deep thinking power is divided among 5 targets, each target only gets 20% of the attention; if there are 2 targets, each target can get 50% of the attention.

Using effective AI prompts is crucial in achieving clear targets, ensuring the AI’s attention is focused and productive. When AI responds to prompts, its effectiveness is greatly influenced by the clarity and specificity of the input.

• Bad case: ”Please talk about the impact of AI on programmers”. This example causes the model will be forced to guess - does the user want to know the impact on the industry? the impact on the job? the impact on technology? the impact on employment? With scattered attention, it is easy to generate a vague and general response.
• Good case: “From the perspective of software quality, please talk about the impact of AI on automated testing and how to reduce the bug rate.“ By crafting prompts with specific details, you can avoid vague and general responses. Providing more context in your prompts can significantly enhance Deepseek's ability to generate accurate and relevant responses.

Second: Structure in AI Prompt Creation

After clarifying the target, DeepSeek’s attention needs to focus on problem-solving, which can be done in two ways: Self-exploration or Following the trail, both of which require carefully crafted prompts. Obviously, the latter can greatly improve the efficiency of problem-solving.

Structured prompts

• Hierarchical markdown prompts: can trigger DeepSeek's sequence labeling perception, thereby improving the computing speed.
• Step-by-step prompts: can activate the AI’s hierarchical reasoning module, allowing it to directly enter the process analysis mode. Effective prompt creation is essential for these structured prompts to work optimally.

A prompt engineer plays a crucial role in designing these structured prompts to elicit the best responses from AI systems. Prompt engineering plays a crucial role in improving the efficiency of problem-solving by carefully crafting prompts to write effective prompts that elicit the best responses from AI systems.

• Bad case: “I am a product manager, help me write a requirements document for an AI programming product.”
• Good case: “I am a product manager, please help me write a requirements document for an AI programming product following the steps below. Conduct user pain point research on AI programming; Conduct competitor research based on the user pain point research; Based on the competitor research, provide the TOP 3 core requirements; Complete the functional design based on the TOP 3 core requirements; Complete the UI prototype and interaction diagram based on the functional design.”

AI prompt creation involves these steps to ensure the prompts are effective and tailored to specific tasks.

Third: Details in Crafting Prompts

Details can improve the accuracy of AI-generated content by adding constraints. Detailed prompts can also help generate ideas by providing a clear framework for DeepSeek to follow. If details are lacking, it will greatly expand the generative AI tool’s possibility to explore space and scatter its attention. If details are added, the additional constraints can greatly compress the exploration space and eliminate long-tail calculations. Details can also activate the model’s instance memory library, improving the relevance of the output.

Keeping prompts concise and to the point can also help in maintaining the AI's focus and improving the relevance of the output. Understanding the writing process is crucial, as detailed prompts can aid in overcoming writer’s block and enhance creativity, making it easier to write prompts that yield effective results.

• Bad case: “Write a short story about love” (relatively abstract).
• Better case: “Write a 500-word modern romance story about two college students meeting at a coffee shop” (basic details).
• Best case: “Write a 500-word contemporary romance story about two tech startup founders who meet at a Silicon Valley networking event, incorporating themes of work-life balance, startup culture, and digital communication. Include specific details about their competing AI companies and a pivotal scene during a product launch” (advanced details).

Writing AI prompts with a conversational approach can enable better interaction and more accurate results from AI tools.

The three core principles of an effective prompt are not independent of each other: Clarity defines the target vector direction, Structure builds the reasoning coordinate axis, and Details provides the positioning anchors, all of which are essential in writing prompts that yield precise outputs.

Final DeepSeek AI Prompt Examples

The Flow:

Clear Intent → Multiple Structured Paths → Detailed Constraints → Precise Outputs

Here's a real example using this flow:

Clear Intent:
"Create a marketing strategy for a new fitness app"

Structured Paths:

1. Target Audience Analysis
2. Competitive Analysis
3. Channel Strategy
4. Content Plan

Detailed Constraints for each path:

1. Target Audience:
2. • Focus on millennials aged 25-35
   • Urban professionals
   • Health-conscious but time-limited
2. Competitive Analysis:
3. • Compare with top 3 fitness apps
   • Focus on unique features
   • Identify market gaps
3. Channel Strategy:
4. • Primary: Instagram, TikTok
   • Secondary: LinkedIn, Email
   • Budget allocation per channel
4. Content Plan:
5. • Video formats and length
   • Posting frequency
   • Influencer collaboration specs

Everything, the prompt can only play its maximum role if it adapts to the cognitive model of the AI. Know the “what” and the “why”. The thought process is more important than the conclusion. Writing effective AI prompts is essential to ensure the desired outputs are achieved.

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