A shared tag with AI prompts and code snippets
From workspace: DeepSeek
Team: Main
Total snippets: 11
11 snippets
Use temperature 0.5–0.7 and top-p 0.95 for stable output.
Set the temperature within the range of 0.5-0.7 (0.6 is recommended) to prevent endless repetitions or incoherent outputs. Also, a top-p of 0.95 is recommended.
Use plain language to clearly state what you want.
Write your instructions in plain language, clearly stating what you want. Complex, lengthy prompts often lead to less effective results.
Use tags or markdown to organize prompt sections.
Break up different parts of your prompt using clear markers like XML tags, markdown formatting, or labeled sections. This organization helps ensure the model correctly interprets and addresses each component of your request.
Use <think> if the model skips reasoning.
On rare occasions, DeepSeek-R1 tends to bypass the thinking pattern, which can adversely affect the model's performance. In this case, the response will not start with a <think> tag. If you see this problem, try telling the model to start with the...
Use ‘reason step-by-step’ + \boxed{} for math problems.
For mathematical problems, it is advisable to include a directive in your prompt such as: "Please reason step by step, and put your final answer within \boxed{}."
Define what makes the response correct or useful.
Paint a clear picture of your desired outcome. Describe the specific characteristics or qualities that would make the response exactly what you need, allowing the model to work toward meeting those criteria.
Avoid system messages, use only the user prompt.
Avoid adding a system prompt; all instructions should be contained within the user prompt.
Generate multiple responses and pick the most common.
When evaluating model performance, it is recommended to generate multiple solutions and then use the most frequent results.
Avoid examples, describe the task and output format instead.
Do not provide examples in the prompt, as this consistently degrades model performance. Rather, describe in detail the problem, task, and output format you want the model to accomplish. If you do want to provide examples, ensure that they align...
Explicitly state all task constraints and limits.
When your request has specific limitations or criteria, state them explicitly (like "Each line should take no more than 5 seconds to say..."). Whether it's budget constraints, time limits, or particular formats, clearly outline these parameters to...
Don't ask the model to 'reason step-by-step'—it does by default.
Since these models always reason prior to answering the question, it is not necessary to tell them to "Reason step by step..."