Impact Analysis of a Ratings Feature: Architectural Discipline in Feature Development

A ratings system is not a UI enhancement. It is an architectural mutation.

In this article, we apply disciplined reasoning inside Claude Code to evaluate the impact of introducing a 1--5 star rating feature in Platiflix, strictly limited to backend and frontend layers. No mobile scope. No improvisation. Only structural clarity.

The Illusion of Simplicity

Five stars look harmless. But in a Clean Architecture system (FastAPI → Service → Repository → PostgreSQL, with a Next.js frontend), every new feature modifies contracts, data flows, invariants, and performance characteristics.

The correct sequence is not:

• Add column
• Expose endpoint
• Render stars

The correct sequence is:

• Understand impact
• Validate architecture alignment
• Plan controlled evolution

Claude Code becomes the medium through which structured reasoning happens.

Starting With Context, Not Guesswork

Before asking for analysis, architectural memory was already loaded. The system's folder structure, patterns, and dependencies were documented.

Prompt used conceptually:

Analyze the impact of implementing ratings (1--5 stars). Focus only on backend and frontend. No mobile layer. No speculative features. Just bounded analysis.

This constraint is not cosmetic --- it is architectural hygiene.

Extended Reasoning as Engineering Discipline

Earlier Claude versions exposed explicit levels like think, think deeply, and ultrathink. Modern versions consolidate this into Thinking On/Off (via /config or Alt + T). The interface changes. The necessity of deep reasoning does not.

Extended reasoning enables:

• Component mapping
• Risk anticipation
• Dependency identification
• Complexity estimation
• Sequencing of implementation phases

This is not verbosity. It is structural coherence under uncertainty.

Backend Impact Surface

Database

A rating feature implies decisions:

• One rating per user per course?
• Aggregation computed dynamically or cached?
• Soft deletes or immutable rating history?

Likely structural additions:

• ratings table
• Foreign keys to users and courses
• Composite unique constraint (user_id, course_id)
• Indexes for aggregation queries

Indexes are architectural decisions, not performance afterthoughts.

Domain Layer

The domain must enforce invariants:

• Rating range validation (1--5)
• Idempotent updates
• Anti-duplication constraints

Business truth must reside in the backend --- never delegated to UI assumptions.

API Contracts

Endpoints must remain REST-consistent and version-safe:

• POST /ratings
• PUT /ratings/{id}
• GET /courses/{id}/rating

Backward compatibility must remain intact. A feature must extend architecture --- not destabilize it.

Frontend Impact Surface

Frontend implications include:

• State management updates
• Fetch logic adjustments
• Optimistic vs confirmed UI updates
• Rendering aggregated averages
• Error feedback loops

A star icon is trivial. A consistent UX state model is not.

Scope Correction: Excluding Mobile

Initial analysis included iOS and Android. A refinement prompt removed them. This required no advanced reasoning --- only clarity of instruction.

Scope discipline prevents architectural noise.

Skills Reinforced

This workflow strengthens:

• Context engineering
• Architectural reasoning
• Structured prompt design
• Feature scoping discipline
• Technical documentation literacy

Claude Code does not replace thinking. It enforces its structure.

Impact Analysis as Risk Compression

Without analysis, features introduce entropy. With analysis:

• Effort becomes estimable
• Risks become visible
• Dependencies become explicit
• Implementation becomes phased
• Testing becomes intentional

A ratings feature is small in interface. It is large in structural implications.

Final Reflection

There is intellectual calm in watching a system be analyzed before it is modified. Claude Code, when used correctly, becomes a mirror --- reflecting architectural consequences before they materialize in production.

It does not write your system for you. It helps you see it clearly. And clarity, in software, is leverage.

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Written by Cristian Sifuentes Full‑Stack Engineer · AI‑Assisted Systems Thinker