Structured Prompt Templates for DeepSeek

After experimenting, I found that models like DeepSeek really benefit from structured prompts. Why? Structured prompts help the model think and generate responses more clearly. The front half of the prompt supports the model’s reasoning, while the latter part aligns with human expectations. Effective prompts greatly impact output quality and consistency.

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1. General Overview

After extensive testing, I’ve come to a clear conclusion: models like DeepSeek benefit significantly from structured prompts.

Why does this matter?

Because structured prompts guide the model to think and generate content more effectively. The front portion of a structured prompt helps initiate the model's reasoning process, while the latter half enables better alignment with human expectations. The impact of structured prompts on model output is substantial—the difference between results with and without structure is dramatic.

2. Common Problems with Poor Prompts

Recently, I’ve seen many prompts that perform poorly on DeepSeek. After reviewing dozens of examples, I’ve summarized the main reasons:
  1. Too detailed or over-constraining, which limits the model’s ability to reason independently.
  1. Includes excessive low-value content, which dilutes useful information.
  1. Overly rigid output formatting, which restricts the model’s flexibility in expression.

3. How to Design a Structured Prompt

Basic Template

Role-Based Template (Optional)

Why HTML Tag Format?

Pros:

  1. No need to learn Markdown syntax, easier for non-technical users.
  1. Has clear logic and supports nesting (tags within tags), which models can easily understand.

Cons:

  1. Slightly more verbose than Markdown.
  1. Requires tagging discipline and structure, which may feel cumbersome at first.

Markdown-Compatible Version

If needed, you can also write in Markdown-style with simple bullets and bold/italic formatting. For example:

4. Section-by-Section Writing Guide

4.1 Goal

How to write the Goal section:
  1. Clearly state the task objective.
  1. Avoid long-winded introductions—go straight to the point.
  1. Use action-oriented verbs like "analyze", "solve", "design".
  1. Use + to chain related sub-goals if needed.
Example:

4.2 Context

How to write the Context section:
  1. Include only necessary info for the task.
  1. Prioritize information—put key points first.
  1. Use short phrases instead of long sentences.
  1. Use bullet-style inputs for clarity.
  1. Keep information well-organized.
Example:

4.3 Expectation

The expectation section is not just about format; it helps guide the model’s reasoning chain.
Tips:
  1. Be clear about the desired structure or form of the output.
  1. Avoid step-by-step thinking that overly constrains the model.
  1. Use + to connect reasoning goals or multiple expectations.
  1. Use meta phrases like “explain why”, “make a recommendation” to open up the reasoning space.
Example:

4.4 Role (Optional)

This section allows you to assign a role or persona to the model. Simply wrap the content in a <role:...> tag.

4.5 Optional Components

Depending on task complexity, you may add optional modules such as:
Tip: Use only when necessary. Too many components may overload the prompt and hurt performance.

5. Underlying Design Principles

5.1 Preserve Domain Language

Always keep domain-specific terms or technical keywords. These carry high informational value and aid the model’s reasoning.
Example:

5.2 Prioritize Information Types

When curating context, follow this priority:
Task keywords > Constraints > Domain terms > Metrics > Contextual background

5.3 Preserve Semantic Units

Don’t break semantic units. Instead of separating terms, keep them together.
Example:

5.4 Minimize Over-Instruction

Avoid step-by-step reasoning templates like:
First calculate A, then evaluate B...
Such formats restrict the model's independent reasoning. Instead, use prompts that hint at the goal and let the model build its own logic chain.

6. Application Examples

6.1 Math Application

Task:
Structured Prompt:

6.2 Code Generation

Task:
Structured Prompt:

6.3 Sentiment Analysis

Task:
Structured Prompt:

6.4 Strategic Decision-Making

Task:
Structured Prompt:
 
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