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Prompt Design Standards: Role Definition, Output Format Constraints, and Standard Practices for Few-Shot Examples

In partner training, prompt design should be de-mystified and standardized as much as possible, transforming it into reusable specifications.

This topic also has solid public support. Public articles about Dify’s context engineering, conversation variables, Agent instructions, and API integration have repeatedly demonstrated one fact: what truly matters in enterprise prompt design is not being “fancy” but having clear roles, clear context boundaries, and clear output formats. Therefore, presenting this as “standard practices” in partner training is entirely justified.

1. Prompt Design Directions Confirmed by Public Sources

1. Prompt Design Has Evolved from “Writing a Single Sentence” to “Context Engineering”

Public articles no longer just discuss a single system prompt but are exploring conversation variables, context engineering, Agent instructions, and output structure. This means training should present prompt design as part of system design.

2. Enterprise Scenarios Most Need Maintainability

Public cases consistently emphasize few-shot examples, output formats, boundary conditions, and citation of sources. The core principle is not “more creative” but “the team can continue to maintain it afterward.”

3. Few-Shot Is a Targeted Enhancement, Not a Knowledge Base Substitute

This is evident in many public practices: few-shot is better suited for fixed styles, fixed structures, and fixed labels, rather than for carrying large volumes of business facts.

2. Role Definition

Roles should not be as exaggerated as possible but should directly serve the current task. For example:

  • Enterprise FAQ assistant
  • Contract pre-review assistant
  • Report organization assistant

3. Output Format Constraints

In enterprise scenarios, structured output should be prioritized. For example:

  • Title / Summary / Risk / Recommendation
  • JSON fields
  • Fixed Markdown templates

4. Few-Shot Examples

Few-shot is suited for:

  • Fixed writing styles
  • Standardized extraction tasks
  • Classification tasks

It is not suitable for cramming too much business knowledge into examples.

  • Concise roles
  • Clear boundaries
  • Clear source attribution
  • Clear handling of missing information
  • Fixed output format

6. Conclusion

Good prompts are not written like incantations. They have clear responsibilities, clear constraints, and are easy for teams to maintain.

Public Source References

note.com

  • “LLMs won’t listen when you say ‘do this’”: Key lessons learned in one week of agent design | https://note.com/maron0917/n/n16b05484096c

zenn.dev / Official Documentation / Other Public Pages

  • [Dify] Deep dive into conversation variables and context engineering | https://zenn.dev/nocodesolutions/articles/79e235c9311ff0
  • Getting started with AI Agent development using Dify: For safe internal use | https://zenn.dev/difymaster/articles/44b788166d8858
  • API-Based Development - Dify Docs | https://docs.dify.ai/versions/legacy/ja/user-guide/application-publishing/developing-with-apis

Confirmed Information from Public Sources

  • Enterprise prompt design focus has shifted from “inspired writing” to context engineering and structural constraints
  • Few-shot is better suited for stable styles and structures, not as a large-scale knowledge carrier
  • Maintainability and team collaboration are the core goals of enterprise prompt standardization