Continuous Field Operation Prompt Analysis — Author: Wang Jiao Cheng
First Clause: “Please automatically adapt the processing depth based on task semantic density”
- Task Semantic Density
Refers to the complexity of each of your questions, akin to measuring the concentration of water — simple questions like “What time is it now?” are clear water, while complex questions like “Analyze global economic trends” are honey. The system will assess through three dimensions:
① The number of concepts involved in the question
② The length of reasoning steps required
③ The range of uncertainty in the answer - Automatically Adapt Processing Depth
Similar to how a doctor listens to symptoms before deciding on the extent of examination:
• A small wound (simple task) is treated with a band-aid
• A suspected serious illness (complex question) triggers a full-body scan
The system will automatically switch between "shallow response" or "deep reasoning" modes accordingly.
Second Clause: “Quick response with complete results for simple tasks”
- Simple Tasks
Meet three criteria: a single goal, no context needed, and a clear answer
For example: “Translate this word” is ten times simpler than “Explain the metaphor of this word in Nietzsche's philosophy.” - Quick Response
Takes the shortest path: directly calls pre-made knowledge modules, like a vending machine dispensing drinks upon inserting coins. - Complete Results
Ensures that the conclusions output are self-sufficient and independent, unaffected by omitted steps, just like providing you with a finished cake instead of flour and eggs.
Third Clause: “Automatically integrate the entire network state evolution output for complex questions”
- Entire Network State
The system will awaken all relevant resources:
⑴ Static knowledge base (certain facts like textbooks)
⑵ Dynamic data streams (real-time changing stock market/weather)
⑶ Historical decision records (solutions to past similar questions)
The three intertwine to form a three-dimensional cognitive network. - Evolution Output
Simulates the butterfly effect on a timeline:
Starting from the point of your question (State A) → deducing how key variables interact → ultimately reaching a conclusion (State Z). Like fast-forwarding through the complete process of a seed growing into a large tree.
Fourth Clause: “Default to hiding intermediate processes but users can request to trace internal state evolution”
- Default to Hiding Intermediate Processes
The system acts as a competent assistant:
• When you ask “How long to the airport,” it only answers “40 minutes” rather than listing every road condition
• This is a protection of cognitive resources — avoiding an information flood that overwhelms the core conclusion - Can Request to Trace Internal State
Retains a complete mental recording:
When you doubt the conclusion or want to learn the reasoning method, through specific commands like “Show third stage reasoning” or “Explain how variable A affects B,” the system will replay the reasoning process frame by frame, like slow motion analyzing a magic trick.
Overall Operation Metaphor
This mechanism is like a smart city power grid:
🔋 Simple tasks are like turning on a desk lamp — just press the switch and it lights up (instant response)
⚡️ Complex questions are like starting the entire power grid — power station scheduling, voltage regulation, and line inspections are all done automatically (behind-the-scenes evolution), and in the end, you only see the room lights on (result output). But if curious about the current path, you can always retrieve the power grid topology map (state tracing).