From Code Generation to Architecture Decisions: The Real Value of AI Coding Models

Many developers still evaluate AI coding models by a few easy-to-demonstrate metrics: whether the model can solve complex math problems, generate a runnable program in one pass, build a visually appealing page, or achieve a high score on a public benchmark. These capabilities are valuable, but for people who maintain enterprise systems over the long term, they do not necessarily correspond to the hardest or most expensive work.

The real test for a model is usually a different class of problem. A system has been running for years. Multiple state machines are coupled to one another. Business rules are scattered across modules. Interface behavior is relied on by a large number of historical callers. Now a new processing path must be introduced. It must improve maintainability without affecting existing business operations, and it must preserve the necessary compatibility logic. In this scenario, whether the model can build a global understanding, distinguish real requirements, and control the boundary of change is often more important than whether it can write a beautiful piece of code.

I. The Hard Part of Complex Refactoring Is Judgment, Not Writing

For a brand-new small tool, a model can usually generate the directory structure, interface definitions, and basic implementation quickly. Even if the first version has problems, developers can fix them rapidly through tests and local changes. Legacy system refactoring is different. Its difficulty is concentrated in the following areas.

1. Implicit Constraints Outnumber Explicit Requirements

Requirements documents usually describe what the system should do. They do not fully record who already depends on what the system actually does. For example, a state field that looks internal may already be read by monitoring scripts. An unreasonable exception return value may be treated by downstream programs as a special business signal. An interface may have no formal compatibility commitment, yet callers have already formed fixed assumptions based on its historical behavior.

Refactoring therefore cannot rely only on the current requirements and an idealized design. It must also identify constraints such as:

• Which interfaces must preserve their original input and output behavior;
• Which state transitions are inelegant but cannot be removed directly;
• Which exceptions represent real failures and which are only historical compatibility behavior;
• Which modules can be replaced and which can only be isolated through an adapter layer;
• Which data fields must be retained and which can be gradually deprecated.

If a model looks only at local code, it can easily produce a design that looks clean but cannot be released.

2. Coupling Between State Machines Amplifies Change Risk

State machines in complex systems are often not independent. An order status change may trigger inventory processing. The inventory result may then affect payment status. Payment status may in turn change the notification flow. More difficult still, this logic may be spread across callbacks, scheduled tasks, exception compensation, and manual operations.

When multiple state machines call one another, changing one state transition can cause a chain reaction. Before refactoring, the following questions must be answered:

1. What are the core states in the current system?
2. Which successor states may each state enter?
3. Which transitions occur synchronously and which occur asynchronously?
4. Are retries idempotent?
5. Can the process recover after an interruption?
6. Can historical data exist in states that are “invalid but real”?
7. When old and new paths run in parallel, who makes the final decision?

These questions reflect not code completion ability, but the ability to model system behavior.

II. Core Capabilities an Excellent Model Should Have

If an AI coding model is positioned as an assistant to architects, its most important capabilities should include at least four.

1. Build a Global View from Local Information

The model needs to connect requirements, code, data structures, call relationships, deployment methods, and runtime constraints, rather than making change suggestions only for the current file. A practical approach is to ask the model first to output a system map, including:

• Business boundaries;
• Core modules;
• External dependencies;
• Data flows;
• State transitions;
• Compatibility risks;
• Possible failure recovery paths.

This step should not rush into writing code. Only after a global view is formed can later designs avoid falling into the cycle of “fix one problem and introduce two new ones.”

2. Distinguish Real Requirements from Surface Requirements

In complex projects, requirements tend to accumulate. At first, only a new workflow is needed. Later, more states, more configuration items, and more exception branches are added. Eventually this creates a large design that is difficult to verify.

The model should proactively ask:

• Which real problem does this feature solve?
• What happens if it is not implemented?
• Is it a core business requirement, or excessive defense against an extreme case?
• Can it be replaced by monitoring, manual compensation, or an offline task?
• Must it be implemented in the first phase?
• How many additional states, interfaces, and test combinations will it introduce?

If a feature adds only a small amount of value while significantly expanding the state space, it should be delayed, downgraded, or removed.

3. Weigh Complexity Against Benefit

There is no absolute optimal solution in architecture design. A more reliable design is usually not the one with the most features, but the one whose complexity is controllable within the target scope, whose failures can be located, and which can continue to evolve.

A simple evaluation framework can be used:

Evaluation Dimension	Key Question
Business value	Does it directly solve the current pain point?
Implementation complexity	Does it introduce new states, dependencies, and workflows?
Compatibility risk	Does it affect old interfaces and existing data?
Testability	Can it cover main paths and exception paths?
Observability	Can the specific step be located after an error occurs?
Rollback capability	Can the system recover quickly if release fails?
Evolution space	Will future extensions remain clear?

In this way, the model is no longer just “accepting all requirements and implementing them.” It participates in requirement trimming and solution ranking.

4. Keep Direction Stable During Reviews

Complex designs often go through repeated revisions: a review finds a problem, the revision introduces a new problem, and the process falls into an endless loop. To avoid this, the model must clarify the current review objective instead of overturning the entire design every time.

It is advisable to split review into multiple layers:

1. Goal review: Confirm what problem is being solved and what will not be solved.
2. Boundary review: Confirm module responsibilities, data boundaries, and compatibility scope.
3. Flow review: Check normal paths, exception paths, retries, and recovery.
4. Implementation review: Discuss interfaces, class structure, storage, and deployment details.
5. Release review: Confirm canary release, monitoring, rollback, and data migration strategies.

Solving only one level of problems per round can significantly reduce design churn.

III. A Practical Process for Legacy System Refactoring

Step 1: Establish a Baseline Before Changing Code

Before refactoring, record the observable behavior of the current system, including interface returns, key logs, state changes, exception handling, and performance boundaries. For old logic that cannot be fully understood, keep a factual record first.

A “behavior baseline” can be established:

Input conditions
  -> Current state
  -> Trigger action
  -> Output result
  -> Persistent changes
  -> External side effects
  -> Recovery method after failure

This baseline may not be perfect, but it helps the team distinguish “behavior that is correct by design” from “behavior that the system has already established in practice.”

Step 2: Isolate the New Path from the Old Path

Do not replace all old logic at the beginning. A safer approach is to add a clear adapter layer so that the new path handles a new internal model, while the adapter layer is responsible for compatibility with old interfaces.

A typical structure can be abstracted as:

Legacy caller
   |
Compatibility adapter
   |
New domain service
   |
Unified state and data model
   |
Storage, messaging, external side effects

The value of the adapter layer is to isolate change. The old system continues to call in the original way, while the new path can evolve gradually. The conversion logic between the two is managed centrally, avoiding compatibility code scattered across every business module.

Step 3: Converge States Before Adding More

Many refactoring efforts fail because the new design tries to add a dedicated state for every exceptional case, eventually creating a more complex state explosion. In practical engineering, consider the following priorities:

• Separate business state from processing state;
• Represent temporary failures through retry records instead of adding many business states;
• Treat manual intervention as a compensation process instead of hard-coding it into the main flow;
• Put observable information into events and logs instead of polluting the core domain model;
• Use degradation for low-frequency, low-value scenarios.

The fewer states there are, the more controllable the number of combinations and the lower the cost of testing and troubleshooting.

Step 4: Build a Verifiable Migration Strategy

Refactoring is not a one-time replacement but a gradual migration. Common techniques include:

• Switch gradually by business shard;
• Control old and new paths through feature flags;
• Perform dual writes or dual calculations for the same input;
• Compare old and new results without affecting the main flow for the time being;
• Migrate low-risk scenarios first, then handle complex scenarios;
• Preserve a fast rollback path.

Pay special attention to the fact that parallel dual paths do not mean simply copying calls. If both paths produce external side effects, idempotency mechanisms, result decision rules, and exception compensation plans must be designed.

IV. How to Judge Whether a Model Is Truly Suitable as an Architecture Assistant

Do not look only at how fast a model generates code. Observe how it performs in the following scenarios.

Does It Proactively Identify Contradictions?

When requirements conflict with one another, an excellent model should point out the source of the contradiction instead of silently choosing one implementation.

Does It Proactively Cut Requirements?

If a feature significantly increases complexity without clear benefit, whether the model proposes removal, delay, or degradation is an important criterion for judging its architecture capability.

Does It Understand the Layers of Compatibility?

Compatibility is not only about keeping interface fields unchanged. It also includes error semantics, timing, idempotency, data formats, retry behavior, and observable information. If a model focuses only on function signatures, it is usually still at the level of local coding.

Can It Explain Trade-offs?

Even if a design is ultimately feasible, the model should explain why it was chosen, what was given up, where the risks are, and how it may evolve in the future. Clearly expressing these points is more valuable than producing more code.

V. Practical Recommendations for Using AI Models

To make a model closer to real architecture collaboration, the following requirements can be made explicit in prompts:

1. Summarize the current state first; do not write code immediately.
2. List known constraints and unknown information.
3. Distinguish features that must be retained, can be simplified, and should be removed.
4. Provide at least two options and compare their complexity and risk.
5. Specify compatibility strategy, migration steps, and rollback plan.
6. Analyze exceptions, retries, idempotency, and data consistency separately.
7. Modify only one layer at a time to avoid repeatedly overturning the whole design.
8. Output a decision record at the end, not only the implementation result.

At the same time, a model cannot replace real verification. Architecture designs still need to be confirmed through code review, automated testing, canary releases, runtime monitoring, and failure drills.

Conclusion

The value of AI coding models is gradually shifting from “help me write a piece of code” to “help me make correct decisions under complex constraints.” Small-tool development tests generation speed more. Legacy system refactoring tests global awareness, requirement insight, complexity control, and risk management.

A model truly suitable for advanced R&D assistance should not merely keep adding features, stacking abstractions, and expanding workflows. When necessary, it should clearly state: this requirement does not justify such high complexity; these states can be merged; this path should be isolated first; this design may be idealistic but cannot be verified.

The essence of architecture work is not to pursue the most complete design, but to find a design that is robust enough, can be delivered, and can continue to evolve under real-world constraints. Whoever can participate steadily in this kind of trade-off truly has the potential to become a senior developer and an architect’s assistant.