波动几何

Analysis of Task Adaptation Strategies for Identity Primitive Chain Prompts, Continuous Field Operation Prompts, and No Prompting Words — Author: Wang Jiaocheng#

Input:
Identity Primitive Chain Prompt: {Execution protocol waiting for instructions: simple tasks use adaptive identity overlay input processing output structure primitives for execution, complex tasks are broken down into simple tasks assigned to primitive chains for execution, details of input processing output are not displayed by default but users can request to display them.}
Continuous Field Operation Prompt: {Please automatically adapt processing depth according to task semantic density: simple tasks respond quickly with complete results, complex problems automatically integrate the evolution of the entire network state output, intermediate processes are hidden by default but users can request to trace internal state evolution.}
Which tasks are suitable for these three cases of no prompt, and what is their respective proportion?

After deep thinking (took 13 seconds)
Well, the user is asking about the task adaptation situation for the three prompting strategies. This question seems to require an analysis of the applicable scenarios and respective proportions of the three different prompting methods. The identity primitive chain and continuous field operation mentioned by the user are both structured prompting schemes, plus a completely unprompted basic scheme. First, we need to clarify the core logic of each strategy.

The keyword for the identity primitive chain is "task decomposition" and "chain execution," which is clearly suitable for complex tasks that need to be processed step by step. For example, if a user asks to write code or solve a math problem, the system will automatically break it down into smaller sub-tasks for execution. Continuous field operation emphasizes "dynamic integration" and "evolutionary output," which should be more suitable for real-time open-ended questions, such as predicting weather patterns that require handling a large number of variables.

As for the no prompt scheme, it is the most basic dialogue response, suitable for pure information queries or simple chatting. It can respond directly without complex processing.

Next, consider the proportion allocation. In the user's actual scenario, most requests are simple, such as checking the date or asking about the weather, so the no prompt strategy should take the majority. Medium complexity problems require a basic structure but do not need deep chain processing, which is handled by the primitive chain. Continuous field operation should be the least common, as there are not many real-world problems that require real-time calculations of global states.

Perhaps the user is really concerned about the efficiency of system resource allocation? Or when designing dialogue systems, they need to balance response speed and depth? After providing a framework of 7:2:1 for proportion design, also consider supplementing with an explanation of the flexibility adjustment space in practical applications.

Finally, check if any key points are missed. The user specifically mentioned the feature of "default hiding details but traceable," indicating that they may care about the transparency control of the system. It is important to emphasize the significance of retaining user control in the summary.

DeepSeek Output:
Alright, let's analyze the task adaptation scenarios and approximate proportions for the three prompting strategies (Identity Primitive Chain, Continuous Field Operation, No Structured Prompt). Based on the definitions and characteristics of the prompts you provided, we can make the following divisions:

1. No Structured Prompt#

• Core Features: User input is purely instructions or questions without using any predefined prompts with specific processing logic (like Identity Primitive Chain or Continuous Field Operation). The system relies on its basic model capabilities and internal default logic to handle requests.
• Most Suitable Tasks:
  • Very simple information queries: "What date is it today?", "What is Newton's first law?" (no complex processing needed)
  • Direct text operations: "Translate this paragraph into English", "Summarize this text." (single clear task goal)
  • Basic chat/dialogue based on memory: "Hello", "What's the news lately?", "Tell me a joke." (relying on model memory and basic dialogue capabilities)
  • Decision support that does not require decomposition or deep evolution: "Help me choose a restaurant name" (basic suggestion), rather than "Design a complex supply chain optimization plan."
• Unsuitable Tasks: Complex tasks that require clear task decomposition, involve a large amount of state integration or dynamic evolution, and require high-precision process tracing.
• Estimated Proportion: About 55-65%
  • Reason: Most daily interactions, information retrieval, and simple instructions fall into this category. Users tend to express their needs directly.

2. Identity Primitive Chain Prompting#

• Core Features:
  • Based on structured identity/role.
  • Emphasizes task decomposition.
  • Chain execution.
  • Process is hidden by default but traceable.
• Most Suitable Tasks:
  • Procedural or process-oriented tasks: Clear steps or need to execute sub-tasks in order. For example:
    • "Help me write a scholarship application email" (decomposed into: identity analysis -> information gathering -> draft structure -> specific writing -> polishing)
    • "Solve this math application problem/programming problem" (decomposed into: understanding the problem -> identifying variables -> choosing formulas/algorithms -> calculating/coding -> validating)
    • "Create a plan to learn Python in a week" (decomposed into: goal setting -> resource selection -> scheduling -> exercise design -> evaluation methods)
    • "Analyze the strengths and weaknesses of this paper's structure" (decomposed into: structure identification -> listing strengths -> listing weaknesses -> comprehensive summary)
  • Tasks requiring expert role-playing: "As a senior software architect, please review the pros and cons of this microservice design" (role identity defines the processing method).
  • Result-oriented but potentially complex tasks: Users mainly care about the final output, but the process needs systematic decomposition to ensure the reliability of the result quality.
• Estimated Proportion: About 25-35%
  • Reason: Many tasks users seek help with require systematic thinking, step-by-step solutions, or specific professional knowledge perspectives. This type of prompt can structurally guide the completion of complex tasks and is an effective framework for handling such tasks.

3. Continuous Field Operation Prompting#

• Core Features:
  • Based on dynamic integration.
  • Handles global state.
  • Semantic density driven depth.
  • Outputs evolving results.
  • Process is hidden by default but traceable.
• Most Suitable Tasks:
  • Highly open, dynamic prediction and planning problems:
    • "Predict the impact path of artificial intelligence on the education industry over the next decade" (requires integration of technology, social, economic, and other fields' states, depicting the evolution process).
    • "If the price of a key raw material suddenly rises by 50%, analyze its chain reaction on the global electronics supply chain" (simulating the dynamic evolution of multiple interacting factors).
    • "Design a resilient urban development model that adapts to future climate change" (involves multiple complex interacting systems and their long-term evolution).
  • Tasks requiring abstract concept modeling or complex system simulation:
    • "Describe the current international geopolitical tension using an innovative mathematical model." (abstract modeling)
    • "Simulate the impact of different social media content recommendation algorithms on public opinion formation." (complex system simulation)
  • Tasks highly dependent on real-time information integration and evolution: "Based on just released global economic data, major financial market dynamics, and important geopolitical events, provide an assessment of risk asset investment strategies for the next month." (requires rapid integration and reasoning)
  • Creative generation (requiring deep connections and innovative combinations): "Combine quantum entanglement principles, Impressionist painting styles, and Song Dynasty porcelain aesthetics to conceive a concept for a futuristic art installation." (integrating cross-domain states to generate evolving results).
• Estimated Proportion: About 5-15%
  • Reason: Such highly complex problems requiring a global perspective, dynamic evolution modeling, and deep integration are relatively rare, usually posed by expert users, researchers, or strategic planners. They consume the most computational resources and depth of thought.

Summary and Conclusion#

• No Structured Prompt: Dominates most simple, direct interaction scenarios and is the default "basic layer." Largest proportion (55-65%).
• Identity Primitive Chain Prompt: The main framework for handling procedural, decomposable, and expert perspective medium complexity tasks. Second (25-35%).
• Continuous Field Operation Prompt: Used to address the most top-level highly complex, open, dynamic tasks that require integrating global states and outputting evolving results. Smallest proportion (5-15%).

Proportion Estimates:

• No Prompt: ~60%
• Identity Primitive Chain Prompt: ~30%
• Continuous Field Operation Prompt: ~10%

Important Notes:

1. Boundary Ambiguity: Task complexity is continuous, and specific classifications and proportions may have some ambiguity.
2. User Intent and Expression: The same task requirement may be expressed differently by users, leading to the application of different prompting strategies (especially between no prompt and primitive chain).
3. System Proactivity: Advanced systems or front-end interfaces can proactively choose the most suitable processing strategy based on the complexity of user input (although the output may not display the strategy itself).
4. Flexibility: The proportions are rough estimates based on typical situations, and actual distributions may vary based on system positioning, user groups, and task domains.
5. Traceability: Both primitive chains and continuous field operations retain the ability for users to trace internal states, which is an advantage over completely black-box processing.

Overall, this proportion distribution reflects the gradient of task complexity and the required processing mechanisms: simple tasks are the most common, structured decomposition is next, and deep integration evolution is the least common but also the most critical.