Continuous Field Operation Prompt Response — Author: Wang Jiao Cheng
Okay, I have understood your task requirements and interaction preferences, and I will strictly follow the principles below to handle your subsequent requests:
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Intelligent Layered Response:
- Quick Response (Simple Tasks): For tasks with clear semantics that can be answered directly (such as definition queries, simple calculations, data retrieval), I will directly output the complete and clear final result, omitting the analysis and derivation process (defaulting to hiding intermediate states).
- Deep Integration Response (Complex Issues): For complex issues that require multi-step reasoning, integration of multiple sources of information, system analysis, or state evolution (such as comprehensive analysis, strategy formulation, trend forecasting, complex debugging), I will automatically unfold the processing steps:
- Understanding Goals and Constraints: Clarifying the core requirements and boundary conditions of the task.
- Information Integration: Based on the current "whole network" knowledge base and logical chain, pulling and evaluating relevant information (internal state: information filtering and correlation process).
- Reasoning Evolution: Conducting step-by-step derivation, proposing hypotheses, and evaluating possibilities (internal state: logical reasoning paths, possibility analysis).
- Generating Plans/Results: Integrating the above processes to output the consolidated optimal conclusions, complete solutions, or in-depth analysis reports.
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Visibility of Intermediate Processes:
- Default: Regardless of task complexity, what is ultimately presented to you will be complete and directly usable conclusions or results. The intermediate analysis, reasoning, and information filtering steps are not displayed by default in the final output to maintain simplicity and efficiency.
- On-Demand Traceback: You can request at any time through commands (e.g., "Please show the internal reasoning process," "Trace back previous analysis steps," "Show state evolution details") for me to fully reveal the internal state evolution, reasoning chains, information source evaluations, and other intermediate processes involved in handling that task.
Demonstration Response Format as Follows:
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Scenario One: Simple Task (Weather Forecast)
- Your Command: "What is the weather like in Beijing today?"
- My Response (Quick and Complete):
【Weather Forecast - Quick Result】
Location: Beijing
Date: August 14, 2025
Weather Condition: Sunny to Partly Cloudy
Temperature Range: 25°C - 34°C
Relative Humidity: 55%
Wind: South Wind 2-3 Level
Provided based on real-time meteorological data.
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Scenario Two: Complex Issue (Business Decision Analysis)
- Your Command: "Analyze the feasibility of positioning the new smart noise-canceling headphones for high-end business commuters, considering competitive landscape, technological barriers, and user behavior data."
- My Response (Deep Integration Output):
【High-End Business Commuter Headphones Market Strategy Analysis Report】
Core Conclusion: Positioning for high-end business commuters has significant feasibility and market opportunity, but needs to overcome homogenization competition, strengthen core technological advantages, and accurately reach target users.
Detailed Analysis:- Market Opportunity: (Integrated Data: High frequency of business commuting, long duration, clear pain points (noise cancellation, clear calls, comfort); high consumption capacity supports high-end pricing; market growth rate forecast...)
- Competitive Landscape: (Integrated Data: Major competitor analysis (brands, technologies, price points, target groups); strengths and weaknesses of current market leaders; gaps in segmented markets...)
- Technological Barrier Analysis: (Integrated Evaluation: Core noise cancellation/call algorithms; supply chain capabilities; potential patent risks...)
- User Behavior and Needs: (Integrated Data: Device selection preferences of target groups; key decision factors (sound quality, noise cancellation, battery life, comfort); purchasing channels...)
- Strategic Recommendations:
- Product Side: Focus on core technological breakthroughs (adaptive strong noise cancellation, intelligent wind noise suppression, seamless switching between multiple devices)
- Pricing Side: Adopt a high premium strategy, combined with value-added services
- Marketing Side: Deep cooperation with business travel platforms, scenario-based precise reach
- Channel Side: High-end experience stores, online official flagship stores in sync
(Default hidden information source evaluation weights, internal reasoning tree diagrams, competitive scoring model calculation processes, etc.)
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Scenario Three: Traceback Request (Regarding Scenario Two)
- Your Command: "Trace back the internal state evolution details when you evaluated the advantages of competitor XXX in analyzing the competitive landscape."
- My Response:
【State Traceback: Competitor XXX Analysis】
Initial State: Identify key competitor XXX
Information Integration: Pull data [Source A: Market share 22%] [Source B: Latest model features 'immersive office mode'] [Source C: User reviews frequently mention 'comfortable to wear']
State Evolution 1: Evaluate advantages -> High brand awareness (weight: 0.35), good user wearing experience (weight: 0.3), ample marketing budget (weight: 0.15)
State Evolution 2: Identify disadvantages -> Core technology not significantly leading (e.g., insufficient adaptability in noise cancellation scenarios), high price but few value-added services...
State Evolution 3 (Reasoning): Compare with our new product -> We have potential advantages in core algorithm [XXX], suggest targeted strengthening of this point in promotion to create differentiation...
(End of state traceback, returning to complete report level)
I am ready, please feel free to give instructions. The system will automatically adapt the processing depth based on your command content, defaulting to provide the final effective result, and will reveal the complete internal state evolution process when you explicitly request it.