Applying SOLID Principal to LLM System Design

Remember when you first discovered Large Language Models? The excitement! The possibilities! You probably built something amazing in a weekend, shipped it, and felt like a genius.

Then reality hit. Your "simple" chatbot now handles customer service, generates code, moderates content, and somehow ended up managing your company's inventory. The once-elegant prompt has become a 500-line monster that breaks whenever Mercury is in retrograde.

Sound familiar? You're not alone. The AI powered Software development is repeating every mistake we made in early software development—monolithic systems, tight coupling, and code that nobody dares to touch and understand.

You can read this POST with AI also 

ChatGpt. Perplexity   Claude

Vibe coding also has role to play in this.

But there's hope. Robert "Uncle Bob" Martin's SOLID principles, which revolutionized object-oriented programming, can transform how we build AI systems too.

Single Responsibility Principle: One AI, One Job

"A class should have one, and only one, reason to change."

Problem: Everything AI

We've all seen them—those monstrous services that try to do everything:

java
@Service
public class AIGodService {
    public SentimentResult analyzeSentiment(String text) { ... }
    public List<Product> generateRecommendations(Customer customer) { ... }
    public String handleCustomerInquiry(String inquiry) { ... }
    public void processReturn(ReturnRequest request) { ... }
    // ...and 20 other responsibilities
}

When your sentiment analysis needs tweaking, you risk breaking the recommendation engine. When you update customer service logic, inventory management might explode. It's a house of cards waiting to collapse.

Solution: Specialized Experts

Instead of one AI that does everything poorly, create focused services that excel at specific tasks:

java
@Service
public class SentimentAnalysisService {
    public SentimentResult analyze(String text) {
        // Just sentiment, nothing else
    }
}

@Service
public class ProductRecommendationService {
    public List<Product> recommend(Customer customer) {
        // Only recommendations
    }
}

Think of it like assembling a dream team instead of hiring one overworked intern. Each service becomes an expert in its domain, leading to better accuracy and easier maintenance.

Real-world win: A content moderation system with separate detectors for toxicity, spam, and misinformation. Each can be updated independently without breaking the others.

Open/Closed Principle: Built to Grow

"Software should be open for extension, closed for modification."

Problem: Model Lock-in

Your code probably looks like this disaster waiting to happen:

java
public String generateResponse(String prompt, ModelType modelType) {
    switch (modelType) {
        case GPT_4: return openAI.complete(prompt);
        case CLAUDE: return anthropic.generate(prompt);
        case LLAMA: return ollama.run(prompt);
        // Add new model? Modify this method!
    }
}

Every new model means touching existing code. Every provider change means hunting down hardcoded logic across your entire codebase.

Solution: Plugin Architecture

Design your system like a Swiss Army knife—ready for new tools without rebuilding the handle:

java
public interface LLMPlugin {
    boolean canHandle(LLMRequest request);
    LLMResponse execute(LLMRequest request);
}

@Component
public class CodeGenerationPlugin implements LLMPlugin {
    public boolean canHandle(LLMRequest request) {
        return request.getIntent() == RequestIntent.CODE_GENERATION;
    }
    
    public LLMResponse execute(LLMRequest request) {
        // Specialized code generation logic
    }
}

Want to add GPT-5 support? Create a new plugin. Need to handle a new type of request? Another plugin. The core system never changes.

Real-world win: Adding multimodal capabilities to a text-only system without touching existing code.

Liskov Substitution Principle: True Flexibility

"Objects should be replaceable with their subtypes without breaking things."

The Problem: Fake Abstractions

You create interfaces, but they're just facades hiding provider-specific chaos:

java
// This looks good...
public interface LLMProvider {
    String generate(String prompt);
}

// But implementations leak details everywhere
public class OpenAIProvider implements LLMProvider {
    public String generate(String prompt) {
        // Returns JSON that only works with OpenAI parsing logic
        // Uses OpenAI-specific error codes
        // Expects OpenAI-formatted prompts
    }
}

Swapping providers breaks everything because they're not truly interchangeable.

Solution: Genuine Compatibility

Create abstractions that actually abstract:

java
public interface LLMProvider {
    LLMResponse generate(String prompt, GenerationConfig config);
    Set<LLMCapability> getCapabilities();
}

public class GenerationConfig {
    private final int maxTokens;
    private final double temperature;
    // Standard configuration that works everywhere
}

Now any provider can replace any other provider (within their capabilities) without your application knowing or caring.

Real-world win: Switching from expensive GPT-4 to cost-effective local models for development environments without changing a single line of business logic.

Interface Segregation Principle: Right-Sized Interfaces

"Don't force clients to depend on things they don't use."

Problem: Interface Bloat

One massive interface to rule them all:

java
public interface MegaAIService {
    String generateText(String prompt);
    String generateCode(String description);
    byte[] generateImage(String description);
    VideoResult analyzeVideo(byte[] video);
    // 47 more methods your chatbot will never use
}

Your simple chatbot shouldn't need to import video analysis capabilities.

Solution: Focused Contracts

Break interfaces into logical, cohesive groups:

java
public interface TextGenerator {
    String generate(String prompt);
}

public interface SentimentAnalyzer {
    SentimentResult analyze(String text);
}

public interface CodeGenerator {
    String generateCode(String description, Language language);
}

Now components only depend on what they actually need. Your chatbot imports text generation and sentiment analysis. Your developer assistant adds code generation. Your content moderator might use all three.

Real-world win: Different teams can work on different capabilities without stepping on each other's toes.

Dependency Inversion Principle: Abstractions Rule

"High-level modules shouldn't depend on low-level modules. Both should depend on abstractions."

Problem: Concrete Coupling

Your business logic knows way too much about AI implementation details:

java
@Service
public class OrderProcessor {
    private final OpenAIGPT4Client gpt4Client; // Locked to specific implementation
    
    public ProcessedOrder processOrder(OrderData order) {
        String openAIPrompt = formatForOpenAI(order); // Provider-specific formatting
        OpenAIResponse response = gpt4Client.complete(openAIPrompt);
        return parseOpenAIResponse(response); // Provider-specific parsing
    }
}

This code is married to OpenAI. Divorce is expensive and messy.

Solution: Abstract Dependencies

Your business logic should think in business terms, not AI implementation details:

java
@Service
public class OrderProcessor {
    private final LLMService llmService; // Abstract dependency
    
    public ProcessedOrder processOrder(OrderData order) {
        ProcessingRequest request = ProcessingRequest.builder()
                .data(order.toJson())
                .taskType(TaskType.ORDER_PROCESSING)
                .build();
        
        LLMResponse response = llmService.process(request);
        return parseResponse(response); // Generic parsing
    }
}

Now your order processor works with any LLM implementation. Today it's OpenAI, tomorrow it might be Claude, next week it could be your own fine-tuned model.

Real-world win: Testing with fast, cheap local models while running production on premium cloud models.

What do you gain from it ?

Maintainability

Each component has a clear purpose. No more "change one thing, break everything" scenarios.

Testability

Single-responsibility components are easy to test:

java
@Test
void billingPluginShouldHandleBillingQueries() {
    // Simple, focused test
    assertTrue(billingPlugin.canHandle(billingIntent));
}

Flexibility

Want to swap GPT-4 for Claude? Easy. Need to add a new capability? Just create a new plugin. Requirements change? Your architecture adapts gracefully.

Team Productivity

Different developers can work on different components without conflicts. The frontend team can use mock AI interfaces while the AI team perfects the real implementations.

Common Pitfalls to Avoid

Prompt Soup

Don't create mega-prompts that try to handle every possible scenario. Split complex tasks into focused, manageable prompts.

Model God Object

Avoid services that take a dozen parameters and try to handle every possible AI task.

Tight Coupling Trap

Don't hardcode model names, API formats, or provider-specific logic in your business code.

Getting Started

1. Audit your current code - Look for classes doing multiple things, hardcoded model logic, and monolithic interfaces.
2. Extract abstractions - Define clean interfaces for your AI operations.
3. Create adapters - Implement your interfaces for specific models/providers.
4. Inject dependencies - Let Inversion Control framework/lib wire everything together based on configuration.
5. Add plugins - Create extension points for new capabilities.

The Bottom Line

Organizations building maintainable AI systems today will dominate tomorrow. While others struggle with technical debt from their "quick and dirty" LLM implementations, you'll be shipping new features at lightning speed.

SOLID principles aren't just academic theory—they're battle-tested guidelines that have saved countless projects from architectural nightmares. As AI becomes more central to software systems, these principles become more crucial, not less.

Start small. Pick one principle. Refactor one component. Your future self will thank you.

--------------