Prompt Patterns

Reusable DeepSeek prompt templates for summarization, coding, FAQs, SEO content, and knowledge tasks.

DeepSeek Prompt Patterns

The goal of prompting is not to be clever.
The goal is to be repeatable, reviewable, and useful.

Pattern 1: long-document summarization

Read the following material and return a structured summary.

Audience:
Product manager / decision maker

Goal:
Extract only the information that changes decisions.

Output requirements:
1. 5 key takeaways
2. Key facts or data points
3. Risks
4. Unanswered questions
5. Flag obvious contradictions if present

Material:
{{context}}

Pattern 2: code review

Review the following code as a senior engineer.

Goal:
Find issues that affect reliability, performance, maintainability, and system boundaries.

Output format:
1. Overview
2. High-priority issues
3. Suggested fixes
4. Side effects or tradeoffs
5. Regression test ideas

Code:
{{code}}

Pattern 3: FAQ generation

Build an FAQ from the following material.

Requirements:
1. Return 10 Q&A pairs
2. Questions must be written from the user's perspective
3. Answers should be concise but complete
4. Mark unknown points as "needs human confirmation"

Material:
{{docs}}

Pattern 4: SEO rewriting

Rewrite the following content around the keyword {{keyword}}.

Target page:
Homepage hero / category intro / FAQ / blog summary

Requirements:
1. Keep the core information
2. Remove filler
3. Write naturally without keyword stuffing
4. Add clear subheadings
5. Return a 120-160 character search snippet

Source content:
{{content}}

Pattern 5: knowledge-grounded Q&A

Answer only from the material I provide.

Requirements:
1. Give the conclusion first
2. Then show the supporting basis
3. If the source is insufficient, say "insufficient source material"
4. Do not invent unsupported facts

Question:
{{question}}

Material:
{{context}}

The four most common prompting mistakes

1. The goal is vague

"Take a look at this document" is worse than "return five decision-relevant conclusions for a PM."

2. The context is incomplete

The user knows the background. The model does not.

3. The output format is undefined

If you do not specify the structure, you usually get text that is harder to reuse.

4. There is no second-pass review

Many answers improve dramatically after one follow-up round asking for assumptions, missing information, or edge cases.

Practical recommendation

  • Build fixed prompt templates for recurring tasks
  • Test them with real business examples
  • Define mandatory output fields
  • Ask the model to mark uncertainty explicitly