DeepSeek Model Guide: 10 Practical Tips and Usage Techniques

DeepSeek, developed by Hangzhou DeepSeek AI, offers a compact web and app interface with three interaction modes: the basic V3 model, the reasoning‑focused R1 model, and an internet‑search (RAG) mode that retrieves and augments information before generation.

The V3 model, upgraded to DeepSeek‑V3 in December, matches top‑tier models (e.g., GPT‑4o, Claude‑3.5‑Sonnet) with 671B parameters and excels at fast factual answers, while the R1 model, released in January, provides deeper logical reasoning with 660B parameters but runs slower and requires a paid Pro subscription.

DeepSeek's knowledge base is current up to July 2024; queries about events after that date benefit from enabling the internet‑search mode. The article explains Retrieval‑Augmented Generation (RAG) in simple terms.

Prompt engineering focuses on clear, accurate expression. Basic templates include: 你是谁+你的目标。 and extended forms like 你是谁+背景信息+你的目标。 or 我要xx，做xx用，希望达到xx效果，但担心xx问题…… . Adding a “I am a primary‑school student” tag helps the model simplify explanations.

Practical examples demonstrate how to ask the model to adopt specific writing styles, such as mimicking a celebrity or generating micro‑fiction, and how to combine V3 and R1 for iterative refinement.

DeepSeek supports file uploads (up to 50 files, each ≤100 MB), enabling private knowledge‑base reasoning. The API can be called with model='deepseek-reasoner' , and the full reasoning chain is exposed.

R1’s openness includes publishing its reasoning chain, releasing training techniques (RL‑based post‑training), and open‑sourcing six distilled models ranging from 1.5B to 660B parameters, with links to HuggingFace and the research paper.

The article concludes with a reflective question about the role of AI in humanity’s future, encouraging readers to share their own tips.