takuya

Posted on Jan 7

Three Engineering Skills That Determine AI Programming Quality

#skills #apidesign #frontenddesign #databasedesign

Have you ever experienced this frustrating situation?

The same AI assistant sometimes generates elegant, maintainable code,
while other times produces patched-together "just make it work" solutions.

What causes such dramatic differences in output quality?

After analyzing countless real-world usage scenarios, one clear conclusion has emerged:
The difference in AI capabilities stems more from whether proper engineering skills have been injected, rather than parameter size.

When AI only receives instructions on "how to write code," its limitations quickly become apparent.
However, when it masters transferable engineering skills, its behavioral patterns undergo a qualitative transformation.

The following three skills are the key to evolving AI from "can write" to "can design."

What Are Skills? Why Do They Determine AI Programming Assistant Limits?

In the context of AI programming, skills aren't specific APIs or syntax tricks, but rather collections of engineering decision-making capabilities, such as:

When to abstract and when to keep things simple
How to leave room for collaboration and expansion from the start
How to avoid "works now, hard to change later" designs

AI code generation tools without skills typically exhibit:

Can complete tasks but code lacks structure
Can implement features but difficult to maintain and evolve

Conversely, AI programming assistants with skills begin to show "engineering personality":

Proactively create boundary divisions
Follow implicit industry conventions
Avoid creating future technical debt

First Skill: Frontend Design

What Happens Without This Skill?

Whether using Claude Code, Cursor, or other AI programming tools, code generation lacking Frontend Design skills typically has these problems:

Component responsibilities are confused, with logic and UI tightly coupled
State management is arbitrary, making later refactoring difficult
Pages work but extension costs are extremely high

How Does Having This Skill Change Things?

Frontend Design skills focus not on "how to write pages" but on:

How to split components to achieve reusability
Whether state should be local, global, or server-side
How to decouple presentation and business logic layers

When AI programming assistants possess this type of skill, the generated code typically:

Has clearer structure
More closely resembles real projects rather than sample code
Shows greater flexibility to requirement changes

This is the essential difference between "can write frontend" and "understands frontend design." Notably, modern AI programming tools like Claude Code can demonstrate code quality approaching senior developers in real projects by incorporating such Frontend Design skills.

Second Skill: API Design Principles

If frontend design affects experience quality, then API design determines how far the system can go.

Common Consequences Without This Skill

In actual development, AI lacking API Design skills frequently creates these problems:

Interface numbers continue to expand but semantics are confused
Frontend and backend communicate frequently but rework never stops
When requirements change, touching one affects everything
API documentation doesn't match actual implementation, reducing team collaboration efficiency

What Does API Design Skill Really Solve?

It focuses not on "how to write interfaces" but on:

How to model based on resources rather than functions
Whether URL, Method, and Status Code semantics are consistent
Whether return structures are stable and conducive to long-term collaboration
Whether APIs have evolution space rather than one-time design

Tool Chain Fragmentation: The Biggest Obstacle to API Design Implementation

In real teams, API design often occurs before code. However, this process is full of challenges:

Pain Points of Traditional Tool Chains:

Swagger/OpenAPI: Strong design capabilities but lacks debugging and testing functions
Postman: Perfect testing functionality but design and documentation management are fragmented
Custom Documentation: High maintenance costs, easily diverges from actual implementation

Many teams are forced to switch between multiple tools: design with Swagger, test with Postman, generate documentation with other tools. This fragmented workflow is the fundamental reason why API design principles are difficult to implement.

Value of Integrated Solutions:

Next-generation API development platforms like Apidog solve exactly this core pain point:

Design-as-Testing: Immediate debugging verification while designing APIs
Intelligent Mock: Frontend doesn't need to wait for backend, automatically generates mock data based on design
Document Synchronization: Design changes automatically sync to documentation, maintaining consistency
Team Collaboration: Multi-person real-time editing, avoiding understanding deviations

When AI works in such integrated API design environments, it not only generates interfaces that comply with RESTful specifications, but more importantly, understands the role of the entire API in system architecture, outputting solutions truly suitable for team collaboration.

Third Skill: Postgres Table Design

Database design is the part most easily "compromised" yet most difficult to rework.

Typical Manifestations When Design Skills Are Lacking

Table responsibilities are unclear, fields keep getting added
Over-reliance on JSON, sacrificing query and constraint capabilities
Indexes added randomly, performance problems repeatedly occur

Core Value of Postgres Table Design Skills

This skill emphasizes:

Whether tables have single responsibility
Whether constraints are clearly defined at the database layer
Whether indexes serve actual query paths
Whether data structures support future expansion rather than current compromise

When models possess basic table design capabilities, the generated schemas clearly tend toward:

Maintainable
Evolvable
Long-term usable

Rather than just "can insert and query."

Summary: Skills Determine Whether AI Is a Tool or Engineering Partner

You can think of AI as a high-potential junior engineer:

Without skills, it can only complete explicit instructions
With skills, it begins participating in engineering decisions

Frontend Design, API Design Principles, and Postgres Table Design correspond respectively to:

User-side structural capabilities
System collaboration boundary capabilities
Data long-term evolution capabilities

The upper limit of model capabilities is never determined solely by parameters, but by engineering skills.

When these capabilities are systematically injected, AI truly evolves from
"a tool that writes code"
to
"a partner that understands engineering."

How has your development experience been? If you have impressive experiences with AI programming or thoughts about these skills, please share them in the comments!