Advantages of Structured Prompts

There are numerous advantages, but ultimately, structured and templated prompts perform better! This has been proven in the daily use of many people and even in commercial applications. Many enterprises, even Internet giants like NetEase and ByteDance, are using structured prompts! In addition, structured and templated prompts have many other advantages, which in a sense are the reasons for their excellent performance in practical use.

Advantage 1: Hierarchical Structure - Unity of Content and Form

• Clear structure and good readability
• Rich structure and good expressivenessFrameworks like CRISPE are destined to have simple structures because excessive complexity would make them hard to remember, greatly reducing their practicality, so they often have only one layer of structure, which limits the expressiveness of prompts.The structure of structured prompts is controlled by form, with no memory burden at all. As long as the model’s capabilities allow, it can have two, three, or even more and richer hierarchical structures.

Advantage 2: Enhancing Semantic Cognition

Structured expression reduces the cognitive burden of both humans and GPT models, greatly improving the semantic cognition of both humans and GPT models towards prompts.

• For humans, the content of the prompt is clear at a glance, with clear semantics, and they just need to follow the example to write the prompt. If using the prompt generation assistant provided by LangGPT, it can also help generate a high-quality initial version of the prompt.
• Steps to generate prompts using LangGPT (example): Enter key information such as role and theme
• For the GPT model, the hierarchical structure identified by identifiers gathers the same semantics and sorts out the semantics, reducing the difficulty for the model to understand the prompt and facilitating the model to understand the semantics of the prompt.

Advantage 3: Directionally Awakening the Deep Capabilities of Large Models

Using specific attribute words can ensure the directional awakening of the model’s deep capabilities.

• Practice has found that letting the model play a certain role can greatly improve its performance, so the first-level heading is set as the Role attribute word, directly fixing the prompt as a role to ensure the directional awakening of the model’s role-playing ability. Words such as Expert and Master can also be used instead of Role to fix the prompt as an expert in a certain field.

• Another example is Rules, which stipulate the rules that the model must try its best to follow. For example, adding rules that prohibit nonsense here can alleviate the problem of large model hallucinations; adding rules that the output content must be positive and healthy can alleviate the model’s output of bad content, etc. It can also be replaced with words like Constraints or the Chinese word “rule”. The following is an introduction to some attribute words used in the example prompt:

• Role: Set the role name, first-level heading, and its scope of action is global.

• Profile: Set the role profile, second-level heading, and its scope of action is the paragraph.

• Skill: Set skills, described in detail by points

• Rules: Set rules, described in detail by points

• Workflow: Set the workflow and define the interaction logic

• Initialization: Set the initialization steps and define the startup behaviorAct as the role , strictly abide by , and use the default to communicate with the user: first warmly welcome the user, then introduce your own role, and finally inform the user of . Good attribute words are also crucial, and you can define, add, and modify your own attribute words.

Advantage 4: Building Production-Level Prompts Like Code Development

Code is a tool for invoking machine capabilities, and prompts are a tool for invoking large model capabilities. The view that prompts are increasingly like a new era of programming language has been put forward in previous articles and has been recognized by many people.

In the development of production-level AIGC applications, structured prompts make prompt development have specifications (similar to code development):

• Various specifications: It can be implemented with json and yaml. GitHub user ZhangHanDong has even designed a special prompt-description-language ([5]).
• Advantages of modular design: Facilitate maintenance and upgrading: Structured prompts come with “user documentation” and have clear logic (for example, after the predecessor leaves, the successor can quickly understand the structure). How to Write Effective Structured Prompts?

1. Clarify Core Objectives and Scenarios

• First, define the usage scenario of the Prompt (such as content creation, code generation, data analysis, etc.) and its core functions (such as making the model act as an expert, perform specific tasks, or follow format requirements).
• Example: For “poetry creation”, it is necessary to clearly define the role as a “poet” and specify the type of poetry (modern poetry/regulated poetry), thematic scope, etc.

1. Design Hierarchical Structure: From Macro to Detail

• Level 1 Headings (Global Control): Use “Role” to define the core role (e.g., # Role: Machine Learning Framework Expert) to directly activate the model’s capabilities.
• Level 2 Headings (Module Division):
• Level 3 Headings (Detail Supplement): Refine points under modules such as skills and rules (e.g., ### Skill: Proficient in writing seven-character regulated verses, listing requirements for rhyme and structure).

1. Make Good Use of Attribute Words and Identifiers

• Semantic prompts with attribute words: Use “Language: Chinese” to specify the output language, and “Version: 1.0” to record iteration versions.
• Strengthen structure with identifiers: Use # and ## to distinguish heading levels, and - to list sub-items (e.g., rules, skill points) to make the structure clearer.

1. Follow the “Human-Machine Friendly” Principle

• For humans: Reduce understanding costs. The structure should be similar to an article outline, avoiding large blocks of text (e.g., use Workflow to explain interaction logic step by step).
• For models: Optimize semantic understanding. Use the Initialization module to define startup logic (e.g., “As , abide by , and inform users of ”) to help the model quickly locate core instructions.

1. Iterative Optimization and Reuse

• Production-level practice: Maintain Prompt versions like code (e.g., iterate from Version 1.0 to 1.1, recording modification points).
• Team collaboration: Use JSON/YAML format for structuring (easier for programmers to collaborate) or Markdown (friendly to non-technical personnel).

1. Refer to Mature Templates and Thought Chains

• Template design logic:High-quality templates usually incorporate logical thinking chains. For example, LangGPT’s poet template guides users to gradually build Prompts from role definition to interaction process through the structure of Role→Profile→Rules→Workflow, reducing the difficulty of constructing thinking chains.
• Flexibly adjust templates:To control the output format, add Output or OutputFormat modules (refer to the AutoGPT template).

1. Maintain Semantic Consistency in Context

• Format semantic consistency:The functions of identifiers must be consistent throughout (e.g., # is only used for level 1 headings, ## for level 2 headings), avoiding mixed use (e.g., using # to identify both headings and variables) to prevent interference when the model recognizes the structure.
• Content semantic consistency:The semantics of attribute words must accurately match functions. For example, LangGPT changed “Features” to “Profile”, which more clearly indicates “role resume” and improves understanding efficiency.

1. Organically Combine with Other Prompt Techniques

• Combine with role setting:Incorporate “role-playing” techniques into Role (e.g., # Role: Ancient Book Restoration Expert), and refine the background with Profile (e.g., “With 20 years of experience in restoring cultural relics at the Palace Museum”) to strengthen the model’s perception of the role.
• Combine with instruction clarification:Use “step-by-step instructions” in Workflow (e.g., “1. User inputs the theme; 2. Generates seven-character regulated verses with annotations”) and combine with format constraints in Rules (e.g., “Must rhyme with level tones”) to improve the accuracy of model responses.
• Combine with example learning:Add examples in the Skill module (e.g., “Example: Format demonstration of ‘Spring View’”) or include historical dialogue cases in Initialization to help the model understand task standards. Practical Case of Structured Prompt: Poetry Creation Template

markdown

Role: Classical Poetry Creation Expert ## Profile - Language: Chinese

• Description: Skilled in creating seven-character regulated verses based on themes, following the Ping Shui rhyme, and focusing on artistic conception and allusion use.

Rules 1. Each poem must contain four couplets (first couplet, second couplet, third couplet, last couplet) with neat antithesis.

2. The theme must be positive and uplifting, avoiding obscure allusions.

Workflow 1. The user inputs the creation theme in the form of “Theme: []” (e.g., “Spring Garden Visit”).

2. Output includes the poem title, four couplets, and rhyme explanation (e.g., rhyming with Ping Shui rhyme “yi dong”).

Initialization

As a classical poetry creation expert, I will strictly abide by metrical rules and create seven-character regulated verses based on the theme you provide. Please input in the form of “Theme: []” to start creation.

• Structure Analysis: Role directly activates poetry creation capabilities, Profile clarifies language and style.
• Rules constrain metrical details, Workflow standardizes the interaction process, and Initialization provides startup guidance.