API Requirements and Design

Effective API design starts with careful planning that prioritizes your users' needs over technical convenience.

Let me explain how we designed our AI Prompt Enhancer API, which transforms basic AI prompts into optimized instructions for better responses from models like ChatGPT, Claude, and Gemini.

Start With User Needs, Not Technical Specs

Before writing a single line of code, identify who will use your API and why. This user-first approach prevents you from building features nobody needs while missing capabilities users want.

For our AI Prompt Enhancer, we researched these potential users:

• Frontend developers building AI chat interfaces who need consistent prompt quality without manual intervention
• Content creators who want to optimize prompts for AI tools but lack prompt engineering expertise
• AI application developers who need reliable prompt formatting to improve their app's AI response quality
• Development teams standardizing prompt quality across their organization's AI implementations

Each user group had different priorities. Developers wanted simple integration, content creators needed immediate results, and teams required consistency and monitoring.

Writing Effective User Stories

User stories help you think from your users' perspective using this format:

"As a [type of user], I want [some goal] so that [some reason]."

Here are the actual user stories that shaped our API design:

• Developer Integration: "As a chatbot developer, I want to automatically enhance user prompts so my app produces better AI responses without complex prompt engineering knowledge."
• Content Optimization: "As a content marketer, I want to convert basic instructions into structured prompts to get more consistent AI-generated marketing copy without learning prompt engineering."
• Performance Tracking: "As an AI app developer, I want to track which prompt enhancements work best to optimize my application's AI response quality over time."
• Team Standardization: "As a technical lead, I want to ensure consistent prompt quality across our team's AI implementations so we get predictable results from our AI integrations."

These stories directly influenced our endpoint design and feature prioritization.

RESTful Resource Design: Think Nouns, Not Verbs

A common mistake in API design is creating endpoints based on actions rather than resources.

Our API follows RESTful principles by focusing on resources (nouns) and using HTTP methods to indicate actions.

❌ Bad API Design (Action-Based):

POST /enhancePrompt
GET /getEnhancementHistory  
PUT /updateEnhancement
DELETE /removeEnhancement

✅ Good API Design (Resource-Based):

POST /v1/prompts           # Create and enhance a new prompt
GET /v1/prompts            # List enhanced prompts with pagination
GET /v1/prompts/{id}       # Retrieve a specific enhanced prompt
PUT /v1/prompts/{id}       # Update and re-enhance an existing prompt
DELETE /v1/prompts/{id}    # Remove a prompt from history

This RESTful approach makes our API intuitive because developers already understand how HTTP methods work with resources.

Resource Naming Best Practices

Our API follows these naming conventions:

• Use plural nouns for collections: /prompts not /prompt
• Use lowercase with hyphens: /prompt-templates not /promptTemplates
  Keep names short but descriptive: /prompts not /ai-prompt-enhancements
• Maintain consistency: /auth/token and /auth/validate follow the same pattern
• Apply the same rules everywhere: No exceptions for "special" endpoints

Choosing the Right HTTP Methods

The HTTP method indicates what action to perform on a resource. Here's how we mapped methods to operations:

Real Implementation Example

Here's how our prompt enhancement endpoint works:

// POST /v1/prompts - Create and enhance a new prompt
router.post('/', authenticateToken, rateLimitMiddleware(), async (req, res) => {
    try {
        const { text, format = 'structured' } = req.body;
        
        // Validate input
        if (!text || text.length > 5000) {
            return res.status(400).json({
                error: {
                    code: 'validation_error',
                    message: 'Invalid prompt text'
                }
            });
        }
        
        // Enhance the prompt
        const enhancedText = await promptEnhancerService.enhancePrompt({
            originalPrompt: text,
            format
        });
        
        // Create response object
        const promptObject = {
            id: `prompt_${uuidv4()}`,
            originalText: text,
            enhancedText,
            format,
            createdAt: new Date().toISOString()
        };
        
        res.status(200).json(promptObject);
    } catch (error) {
        next(error);
    }
});

Designing Authentication and Security

Security should never be an afterthought. We planned it from the beginning and implemented multiple layers of protection.

Our Security Strategy

• Token-based authentication: Uses JSON Web Tokens (JWT) for secure access without exposing API keys in client-side code
• API key management: Server-side credentials that never appear in frontend applications
• Rate limiting: Prevents abuse by limiting requests per user and IP address
• Input validation: Validates and sanitizes all inputs to prevent injection attacks
• Error handling: Returns appropriate status codes without exposing sensitive system information

Authentication Flow Implementation

Our authentication system works in four steps:

// Step 1: Client requests token with API key credentials
POST /v1/auth/token
{
    "clientId": "frontend-client",
    "clientSecret": "your-api-key-here"
}
 
// Step 2: Server validates API key and returns JWT token
{
    "access_token": "eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9...",
    "token_type": "Bearer",
    "expires_in": 86400,
    "scope": "api:access"
}
 
// Step 3: Client uses JWT token for subsequent requests
GET /v1/prompts
Authorization: Bearer eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9...
 
// Step 4: When token expires, client requests a new one

Here's our actual token generation code:

// Generate JWT token for authenticated access
function generateToken(payload) {
    return jwt.sign({
        ...payload,
        iat: Math.floor(Date.now() / 1000),
        type: 'access'
    }, process.env.JWT_SECRET, { 
        expiresIn: '24h',
        issuer: 'prompt-enhancer-api'
    });
}
 
// Verify incoming tokens
function authenticateToken(req, res, next) {
    const authHeader = req.headers['authorization'];
    const token = authHeader && authHeader.split(' ')[1];
    
    if (!token) {
        return res.status(401).json({
            error: {
                code: 'missing_token',
                message: 'Authentication token is required'
            }
        });
    }
    
    try {
        const decoded = jwt.verify(token, process.env.JWT_SECRET);
        req.user = decoded;
        next();
    } catch (error) {
        return res.status(403).json({
            error: {
                code: 'invalid_token', 
                message: 'Authentication token is invalid or expired'
            }
        });
    }
}

This approach offers several benefits:

• API keys remain secure in server-to-server communication
• Short-lived tokens minimize risk if credentials are compromised
• Token refresh improves user experience without re-authentication
• Stateless design scales across multiple server instances

Plan for API Versioning From Day One

Once your API is public, changing it becomes difficult without breaking existing integrations. We included versioning from the start, even for internal use.

Our AI Prompt Enhancer API uses URL path versioning:

https://prompt-enhancer.ai/v1/prompts
https://prompt-enhancer.ai/v1/auth/token

When we need breaking changes, we can introduce /v2/prompts Without affecting existing users. Our URL structure includes version information that's:

• Clear and explicit: Version number appears in every endpoint
• Easy to route: Express.js can easily handle version-specific middleware
• Future-proof: We can maintain multiple versions simultaneously

Version Management Strategy

// Route different API versions to different handlers
app.use('/v1', v1Routes);
app.use('/v2', v2Routes); // Future version
 
// Version-specific middleware
const v1Routes = express.Router();
v1Routes.use('/prompts', authenticateToken, promptsV1Controller);
 
const v2Routes = express.Router(); 
v2Routes.use('/prompts', authenticateTokenV2, promptsV2Controller);

Design for Rate Limiting and Error Handling

Rate Limiting Implementation

Rate limiting protects your API from abuse and ensures fair usage. Our implementation includes multiple layers:

// IP-based rate limiting for DDoS protection
const ipLimiter = new RateLimiterRedis({
    storeClient: redisClient,
    keyPrefix: 'rl_ip',
    points: 30,           // 30 requests
    duration: 60,         // per minute
    blockDuration: 300    // block for 5 minutes
});
 
// API key-based rate limiting for authenticated users
const apiLimiter = new RateLimiterRedis({
    storeClient: redisClient,
    keyPrefix: 'rl_api',
    points: 100,          // 100 requests  
    duration: 60,         // per minute
    blockDuration: 60     // block for 1 minute
});
 
// Apply appropriate rate limiter based on request type
function rateLimitMiddleware() {
    return async (req, res, next) => {
        const limiter = req.user ? apiLimiter : ipLimiter;
        const key = req.user ? req.user.clientId : req.ip;
        
        try {
            const rateLimiterRes = await limiter.consume(key);
            
            // Add rate limit headers to response
            res.set('X-RateLimit-Limit', limiter.points);
            res.set('X-RateLimit-Remaining', rateLimiterRes.remainingPoints);
            res.set('X-RateLimit-Reset', new Date(Date.now() + rateLimiterRes.msBeforeNext));
            
            next();
        } catch (rateLimiterRes) {
            res.status(429).json({
                error: {
                    code: 'rate_limit_exceeded',
                    message: 'Too many requests, please try again later'
                }
            });
        }
    };
}

We include rate limit information in response headers so clients can adjust their request patterns:

X-RateLimit-Limit: 100
X-RateLimit-Remaining: 95  
X-RateLimit-Reset: 1626369250

Comprehensive Error Handling

We designed a consistent error response format that provides valuable information without exposing system internals:

// Standardized error response format
{
    "error": {
        "code": "validation_error",
        "message": "The 'text' field is required",
        "details": {
            "param": "text", 
            "reason": "missing_required_field"
        }
    }
}
 
// Global error handler
function errorHandler(err, req, res, next) {
    console.error(`[${new Date().toISOString()}] ${err.message}`);
    
    const statusCode = err.statusCode || 500;
    const errorResponse = {
        error: {
            code: err.code || 'server_error',
            message: process.env.NODE_ENV === 'production' 
                ? getPublicErrorMessage(statusCode)
                : err.message
        }
    };
    
    if (err.details) {
        errorResponse.error.details = err.details;
    }
    
    res.status(statusCode).json(errorResponse);
}

Our HTTP status code mapping:

• 400: Bad Request (validation errors, malformed requests)
• 401: Unauthorized (missing or invalid authentication)
• 403: Forbidden (valid auth but insufficient permissions)
• 404: Not Found (resource doesn't exist)
• 413: Payload Too Large (request body exceeds limits)
• 429: Too Many Requests (rate limit exceeded)
• 500: Internal Server Error (unexpected server-side issues)

Designing with Observability in Mind

Modern APIs need built-in observability for monitoring, debugging, and optimization. Our implementation includes:

Treblle Integration for Production Monitoring

// Real-time API monitoring and analytics
if (process.env.NODE_ENV === 'production') {
    const treblleApiKey = process.env.TREBLLE_API_KEY;
    const treblleProjectId = process.env.TREBLLE_PROJECT_ID;
    
    if (treblleApiKey && treblleProjectId) {
        app.use(treblle({
            apiKey: treblleApiKey,
            projectId: treblleProjectId,
        }));
        console.log('Treblle API monitoring enabled');
    }
}

Common API Design Pitfalls to Avoid

Based on our experience building the AI Prompt Enhancer API, here are the mistakes to avoid:

• ❌ Too many endpoints - Keep your API focused and cohesive rather than trying to solve every possible use case
• ❌ Inconsistent naming - Follow the same patterns across all endpoints; don't make exceptions for "special" cases
• ❌ Missing error handling - Define clear error responses and status codes for every possible failure scenario
• ❌ Poor documentation - Document every endpoint, parameter, and response before you think you're done
• ❌ No rate limiting - Protect your API from abuse with proper limits from day one, not after problems occur
• ❌ Ignoring security - Always use HTTPS and proper authentication; never postpone security for "later"
• ❌ Overexposing implementation details - Hide your internal architecture; clients shouldn't know about your database schema
• ❌ Insufficient validation - Validate all inputs thoroughly; assume all input is malicious until proven otherwise
• ❌ Forgetting CORS configuration - Plan for cross-origin access if you have web clients
• ❌ Not following standards - Leverage existing standards like JWT and OAuth rather than inventing your own

The Design Checklist

Before finalizing your API design, review this checklist based on our AI Prompt Enhancer development:

• ✅ User Stories: Does the API solve the actual needs described in your user stories?
• ✅ RESTful Design: Are resources named consistently using RESTful principles with appropriate HTTP methods?
• ✅ Security Planning: Have you planned authentication, authorization, input validation, and rate limiting?
• ✅ Versioning Strategy: Is versioning incorporated with a clear upgrade path from the start?
• ✅ Error Handling: Have you designed a consistent error-handling strategy with appropriate status codes?
• ✅ Rate Limits: Are rate limits and usage quotas defined for different user types and endpoints?
• ✅ Documentation: Is the API design documented in OpenAPI/Swagger format with examples?
• ✅ Observability: Have you considered monitoring, logging, and health check requirements?
• ✅ Testing Strategy: Can you test all endpoints, error conditions, and security boundaries?
• ✅ Performance Planning: Have you considered response times, caching, and scalability needs?

Takeaways

Effective API design requires considering your users' needs before your technical implementation.

Our AI Prompt Enhancer API succeeded because we:

• Started with real user stories that drove feature decisions
• Followed RESTful principles consistently across all endpoints
• Planned security from the beginning rather than adding it later
• Designed comprehensive error handling that helps rather than frustrates users
• Built-in observability and monitoring from day one
• Created thorough documentation that stays current with implementation

The time invested in proper requirements gathering and design pays dividends throughout the development lifecycle and long after your API goes to production.