Software upgrade in the AI ​​era: How can big models make attendance systems understand human language?

How to make the attendance system "self-taught"

As a software product manager, I have experienced too many scenarios like this:

The customer held up a 30-page "Enterprise Attendance Management System" and said: "Our rules are very simple, which is a flexible working system plus differentiated attendance by department." But when we opened the backend, we saw dense clock-in time configurations, department rule groups, exception conditions... Users need to understand the "rule engine" like programmers, and the implementation team is trapped in endless demand communication meetings.

This is not only a dilemma for attendance systems, but also a microcosm of all enterprise-level software - the more complex the rules, the more difficult it is for users to use .

It wasn’t until the explosion of big model technology that I suddenly realized: What if software could directly “read” corporate systems and understand and execute rules like humans?

In this article, I will take the attendance system as an example to disassemble the big model technology to reconstruct the underlying logic of enterprise software and answer a key question: "When AI begins to 'take over' rule configuration, will programmers be replaced?"

1. Step 1: From “manual translation” to “AI parameter generation”

The biggest pain point of traditional attendance systems is that users need to "translate" natural language rules into system parameters . For example, when a company says "half a day's salary will be deducted if you are late for more than 30 minutes", the implementation staff needs to manually set:

Automatically parse the institutional text and generate structured rule parameters .

▶ Example: Parameter generation for flexible attendance rules

• Input (original text of the company policy): "Flexible clocking-in time on weekday mornings is from 9:00 to 10:00. Overtime clocking-in is considered late. If you are late for more than 30 minutes, 0.5 days' salary will be deducted."

• Large model output (JSON format):

json{ "flexible_rule": { "start_time": "09:00", "end_time": "10:00", "late_threshold": 30, "penalty": 0.5 }}

• System Action: Automatically create a flexible attendance group and fill in the parameters into the database.

Value : Users no longer need to understand terms such as "threshold" and "penalty coefficient", they only need to upload the system document.

❗ Limitations:

Although this method lowers the configuration threshold, it is still essentially a static rule filling . If there is complex logic in the enterprise system (for example, "marketing department field staff do not need to punch in, but need to submit location + customer visit records"), cross-system linkage cannot be achieved by simply relying on parameter configuration.

2. Better solution: let the large model generate "live" logic code

The real qualitative change is to allow the big model to directly generate executable business logic code . For example, for the above marketing department field rules, the big model can generate the following code:

Python

def check_attendance(dept, check_in_time, has_location, has_crm_record): if dept == "Marketing Department":# Field staff judgment logic if has_location and has_crm_record: return "Normal attendance" else: return "Absent" else: # Standard attendance logic for other departments if check_in_time <= datetime.time(9, 30): return "Normal" else: return "Late"

The core advantages of this solution are:

1. Dynamic extensibility: The code can call external system data (such as CRM records, location information).
2. Logical coupling: supports multiple condition combination judgments (such as "positioning + CRM records" are indispensable).
3. Zero manual configuration: From system to code in one step.
   
   

    ▶ Three stages of technical implementation:

1. Semantic understanding: The big model parses the entities (departments, attendance types) and logical relationships in the system.
2. Code generation: Convert natural language into code structures such as functions, conditional judgments, and API calls.
3. Sandbox execution: Run code in a safe environment to avoid system crashes or data leaks.
   

3. Four major leaps from "parameter generation" to "code generation"

The comparison of the two solutions reveals the underlying logic of the large model upgrade software:

▶ Typical case: epidemic home office policy

• The dilemma of the parameter generation solution: dozens of parameters such as "home clock-in exemption conditions" and "salary calculation rules" need to be manually set, and it is impossible to link the health declaration system data.

• Advantages of code generation: Directly generate logic for large models:

Python 

def covid_policy(health_code, is_home_office): if health_code == "red code" and is_home_office: return "normal attendance" elif health_code == "red code" and not is_home_office: return "mandatory home stay" else: return "process according to standard attendance"

4. Programmers and big models: Who rises and falls?

When the big model starts to generate code, a soul-searching question arises: Will programmers be replaced?

The answer is: programmers will not disappear, but the role must evolve .

1.  The core barriers of programmers

• System architecture design: underlying capabilities such as database optimization, distributed computing, and high-concurrency processing cannot be replaced by large models.
• Security and compliance: Key tasks such as code vulnerability scanning, data privacy protection, and audit log design still require manual control.

2.  Positioning of large models

• Business logic translator: Converts user requirements into code, but never touches the core architecture of the system.
• Efficiency Accelerator:
      Traditional development: requirements analysis → coding → testing (2 weeks)
      Large model assistance: demand analysis → model generation code → manual verification (2 hours)

5. The future is here: the era of “autonomous driving” in enterprise software

When big models are deeply integrated into software development, we will see the following trends:

1. Product form: From "function stacking" to "intention understanding", users only need to describe their goals and the system will automatically design solutions.
2. Development mode: From "writing code" to "training models", the core skills of programmers become designing prompts and fine-tuning algorithms.
3. Competitive barriers: From "number of functions" to "data flywheel", the system continuously optimizes its rule generation capabilities through user feedback.

Conclusion: Throw away the "rule translation manual" and return human-computer interaction to its essence

Let’s look back at the example of the attendance system: when the big model takes over the entire process from “understanding the system” to “code generation”, users can finally say to the system just like communicating with colleagues: “Sales department field staff do not need to punch in, but must submit customer visit records every day.” - This is what software should look like .

The ultimate goal of technological revolution has never been to enable humans to learn from machines, but to enable machines to understand humans.