DeepSeek R1 Usage Guide: Prompt Techniques & Practical Tips (2025)

DeepSeek's Emergence and a Landmark Moment for Chinese AI

The emergence of DeepSeek marks a landmark moment for the Chinese AI landscape. This large model—dubbed by many as a "national-level technological achievement"—not only competes head-to-head with OpenAI's top models across multiple authoritative benchmarks but also dramatically lowers the barrier to high-quality AI for everyday users and enterprises through its open-source, free-to-use approach.

This article is based on an in-depth presentation by Professor Shen Yang's team at Tsinghua University. It systematically covers DeepSeek R1's core capabilities and usage methods, while addressing a more critical question: in an era where everyone can use AI, how do you actually use it well?

What Is DeepSeek: An Open-Source, Free Chinese Reasoning Model

DeepSeek is a Chinese artificial intelligence company focused on large model R&D and applications. Its flagship product, DeepSeek R1, is an open-source reasoning model that excels at complex tasks and supports free commercial use.

In authoritative benchmarks such as AIME 2024 and CodeForces, DeepSeek R1 performs on par with OpenAI O1's official release. Notably, AIME (American Invitational Mathematics Examination) and CodeForces are widely recognized as high-difficulty reasoning benchmarks in the AI field—AIME problems require rigorous mathematical derivation, and even top human high school students average only 3–5 points (out of 15); CodeForces is the world's most authoritative competitive programming platform, demanding exceptional code reasoning capabilities. These "adversarial benchmarks" are extremely difficult to game through simple data memorization, making them more reliable indicators of a model's generalized reasoning ability. This is why DeepSeek R1's parity with OpenAI O1 on these two tests carries significant industry implications.

DeepSeek R1's core technical breakthrough lies in: massively applying reinforcement learning during the post-training phase to dramatically improve reasoning capabilities with minimal labeled data, while significantly reducing both training and inference costs. Specifically, DeepSeek R1 employs reinforcement learning algorithms such as GRPO (Group Relative Policy Optimization), enabling the model to continuously optimize its reasoning paths through self-play and reward signals—greatly reducing dependence on human-labeled data. This stands in stark contrast to the traditional high-cost approach of supervised fine-tuning (SFT) with massive human annotations, compressing training costs to a fraction of comparable models.

Compared to international models like ChatGPT and Claude, which impose access restrictions and paywalls for users in mainland China, DeepSeek's open-source nature delivers tangible benefits:

• Individual users: Free access via mobile app or web interface, with zero barriers to entry
• Enterprises and developers: Freedom to use, modify, and deploy locally, with flexible adaptation to business needs
• Industry ecosystem: Drives domestic AI innovation and accelerates deep integration with traditional industries

A Comprehensive Look at DeepSeek R1's Four Core Capabilities

From an architectural perspective, DeepSeek R1's capabilities can be divided into four core pillars. Understanding these will help you choose the right application scenarios in practice.

Knowledge Processing System

This includes general Q&A, domain-specific Q&A, graph-level reasoning, and logical reasoning. DeepSeek R1 particularly excels in mathematical computation and knowledge graph construction, making it ideal for tasks requiring precise derivation.

Natural Language Processing

This covers text analysis, semantic understanding, entity recognition, story creation, code annotation, and other creative tasks—handling everything from content production to code assistance across diverse scenarios.

Interaction Capability Matrix

This includes contextual understanding, multi-turn dialogue, and task execution, with support for deep thinking mode and web search to handle complex problems more efficiently.

Solution Planning

From data visualization and workflow optimization to recommendation generation and personalized preference matching, this fully demonstrates the reasoning model's advantages in complex task handling.

This multi-layered, multi-dimensional capability integration allows DeepSeek R1 to organically combine foundational abilities, supporting rich "AI + industry" application scenarios.

Reasoning Models vs. General Models: Understanding the Difference Is Key to Writing Good Prompts

To truly master DeepSeek R1, you first need to understand the fundamental difference between reasoning models and general models—this directly determines how you interact with AI and the quality of results you get.

Chain-of-Thought: The Core Mechanism of Reasoning Models

The core of reasoning models lies in Chain-of-Thought (CoT) technology. The principle is to have the model generate a series of intermediate reasoning steps before outputting the final answer, simulating the step-by-step thinking process humans use when solving problems. DeepSeek R1 deeply combines CoT with reinforcement learning—the model internally generates a "thinking process" (visible as thinking tags in the user interface), analyzing problems from multiple angles, self-questioning, and verifying before outputting conclusions. This mechanism enables the model to far surpass probability-based general models on tasks requiring rigorous derivation, such as mathematical proofs, logical reasoning, and code debugging.

Core Differences Between the Two Model Types

Dimension	Reasoning Models (DeepSeek R1, O3)	General Models (GPT-4, BERT)
Performance essence	High logical density	High diversity and versatility
Computational principle	Step-by-step Chain-of-Thought reasoning	Probability-based prediction from data
Strengths	Mathematical reasoning, logical analysis, decision-making	Language generation, contextual understanding, open dialogue
Role analogy	Experienced expert	New employee needing guidance

Probability-based models quickly predict answers based on massive training data, while reasoning models derive answers through step-by-step Chain-of-Thought reasoning. As the team puts it: AI has upgraded from an "imitator" to a "thinker."

The Fundamental Shift in DeepSeek R1 Prompt Strategy

Prompt Engineering, as a systematic discipline, has risen rapidly alongside the proliferation of large language models. In the early GPT-3 era, users primarily relied on "few-shot prompting"—including several examples in the prompt to guide model output. However, with the rise of reasoning models, some traditional prompt engineering practices are being overturned. Reasoning models have built-in autonomous decomposition and verification mechanisms, and overly granular prompts can actually "micromanage" the model, constraining its reasoning space. This shift marks a paradigm migration in human-AI interaction from "precise programming-style instructions" to "natural language requirement expression."

This model difference directly determines our dialogue strategy with AI:

For reasoning models (DeepSeek R1): Just say what you want

• Use concise, direct instructions with clear requirements and objectives
• Don't break tasks down step by step—over-decomposition actually limits the model's reasoning logic
• Reduce role-setting phrases like "please act as an expert in XX field" to avoid interfering with the reasoning process
• Essentially, shift from "giving instructions" to "expressing needs"