The Art of the Prompt: Engineering GenAI to Produce SOLID-Compliant Code

We have all seen the magic. You ask ChatGPT to "write a Python script to scrape a website and save it to a CSV," and thirty seconds later, you have working code. It’s impressive. It’s fast. It’s intoxicating.

But then you look closer.

The entire logic—HTTP requests, HTML parsing, data transformation, and file I/O—is jammed into a single 200-line function. Hardcoded dependencies are everywhere. Changing the output format from CSV to JSON requires rewriting half the script.

The AI gave you working code, but it didn't give you maintainable code. It gave you technical debt on day zero.

This is the central challenge of using Generative AI in professional software engineering right now. LLMs are trained on the entirety of the internet’s code, which includes a lot of bad habits. They default to the path of least resistance, which is usually a tightly coupled mess.

If we want to use AI to build serious systems, we have to stop asking it to just "write code" and start asking it to engineer solutions. We do this by baking architectural constraints—specifically the SOLID principles—directly into our prompts.

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The Difference a Prompt Makes: A Case Study

Let's look at a simple requirement: We need a service that fetches user data from an external API and sends a welcome email.

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The Lazy Prompt (and the resulting mess)

Prompt:

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AI Output (The "Junior Dev" Special):

import axios from 'axios'; import * as AWS from 'aws-sdk'; class UserService { async registerUser(userId: string) { // 1. Fetching logic tightly coupled to Axios and a specific URL const response = await axios.get(`https://api.example.com/users/${userId}`); const user = response.data; // 2. Email logic tightly coupled to AWS SES AWS.config.update({ region: 'us-east-1' }); const ses = new AWS.SES(); const params = { Destination: { ToAddresses: [user.email] }, Message: { /* ... boilerplate ... */ }, Source: '[email protected]', }; await ses.sendEmail(params).promise(); console.log('User registered and email sent.'); } }

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Why this fails SOLID:

• Single Responsibility Principle (SRP) Violation: This class is doing two distinct things: fetching data and sending emails. It has two reasons to change.
• Open/Closed Principle (OCP) Violation: If I want to switch from AWS SES to SendGrid, I have to modify this class. I can't extend its behavior without changing its code.
• Dependency Inversion Principle (DIP) Violation: The high-level policy (registerUser) depends heavily on low-level details (Axios, AWS SDK). It's impossible to unit test this without mocking network calls.

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The Engineered Prompt (The "Senior Architect" Approach)

To fix this, we don't need to write the code ourselves. We just need to tell the AI exactly what architectural pattern to follow.

Prompt:

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AI Output (SOLID Gold):

// --- Interfaces (Contracts) --- // The service doesn't care WHERE data comes from (API, DB, file) interface UserRepository { getUserById(id: string): Promise<User>; } // The service doesn't care HOW emails are sent (SES, SendGrid, Log file) interface EmailSender { sendWelcomeEmail(email: string, name: string): Promise<void>; } interface User { id: string; email: string; name: string; } // --- The High-Level Policy (The Application Logic) --- class UserRegistrationService { // Dependencies are injected, not instantiated internally. constructor( private readonly userRepo: UserRepository, private readonly emailSender: EmailSender ) {} async registerUser(userId: string): Promise<void> { // Step 1: Get the user using the abstract repository contract const user = await this.userRepo.getUserById(userId); if (!user) { throw new Error("User not found"); } // Step 2: Send email using the abstract email sender contract await this.emailSender.sendWelcomeEmail(user.email, user.name); console.log(`User ${userId} registered successfully.`); } }

\ Look at the difference. The AI didn't just write code; it designed a small system.

• SRP: The service class now has one job: orchestration. The job of fetching is pushed to a repo implementation; the job of emailing is pushed to a sender implementation.
• OCP: We can now create a SendGridEmailSender class that implements EmailSender and inject it without touching a single line of the UserRegistrationService. The system is open for extension, closed for modification.
• DIP: The high-level module (UserRegistrationService) now depends on abstractions (interfaces), not concrete details. Testing is trivial—we just inject mock objects that satisfy the interfaces.

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The Blueprint for SOLID Prompts

You can apply this to almost any generation task. Here is a checklist for engineering your prompts for architectural quality:

1. Define the Role: Start by setting the context. "Act as a Senior Software Architect obsessed with clean, maintainable code."
2. Name the Principle Explicitly: Don't beat around the bush. "Ensure this code adheres to the Single Responsibility Principle. Break down large functions if necessary."
3. Demand Abstractions: If your code involves external systems (databases, APIs, file systems), explicitly ask for interfaces first. "Define an interface for the data layer before implementing the business logic."
4. Force Dependency Injection: This is the single most effective trick. "The main business logic class must not instantiate its own dependencies. They must be provided via constructor injection."

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Conclusion

Generative AI is a mirror. If you give it a lazy, vague prompt, it will reflect back lazy, vague code. But if you provide clear architectural constraints, it can be a powerful force multiplier for producing high-quality, professional software.

Don't just ask AI to code. Ask it to the architect.

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