As AI becomes a core component of modern applications, developers face new challenges in building reliable, scalable workflows. Through our work with AI developers, we've identified five essential patterns that can make the difference between a prototype and a production-ready AI application.
1. The Chain of Responsibility Pattern
One of the most powerful patterns in AI workflows is the chain of responsibility, where each step in the process handles a specific task and passes results to the next step.
// Instead of this
async function processChatbot(input) {
const context = await fetchContext(input);
const aiResponse = await generateResponse(context, input);
const formattedResponse = formatOutput(aiResponse);
return formattedResponse;
}
// Use this pattern
class WorkflowStep {
constructor(nextStep = null) {
this.nextStep = nextStep;
}
async process(input) {
const result = await this.execute(input);
return this.nextStep ? this.nextStep.process(result) : result;
}
}
class ContextEnricher extends WorkflowStep {
async execute(input) {
const context = await fetchContext(input);
return { ...input, context };
}
}
class AIGenerator extends WorkflowStep {
async execute(input) {
return await generateResponse(input.context, input.text);
}
}
This pattern enables:
- Easy addition of new processing steps
- Better error handling at each stage
- Clear separation of concerns
- Simpler testing and debugging
2. The Retry with Exponential Backoff Pattern
AI services can be unpredictable. Implementing proper retry logic is crucial for reliability.
async function withRetry(operation, maxAttempts = 3) {
for (let attempt = 1; attempt <= maxAttempts; attempt++) {
try {
return await operation();
} catch (error) {
if (attempt === maxAttempts) throw error;
// Exponential backoff with jitter
const backoff = Math.min(1000 * Math.pow(2, attempt), 10000);
const jitter = Math.random() * 1000;
await new Promise(resolve => setTimeout(resolve, backoff + jitter));
}
}
}
// Usage in AI workflows
const response = await withRetry(() =>
aiService.generate(prompt)
);
Key considerations:
- Implement proper backoff intervals
- Add jitter to prevent thundering herd
- Set appropriate timeout limits
- Handle different types of errors differently
3. The Results Cache Pattern
AI operations are often expensive and time-consuming. Intelligent caching can significantly improve performance and reduce costs.
class AICache {
constructor(ttl = 3600000) {
this.cache = new Map();
this.ttl = ttl;
}
async get(key, generator) {
const cached = this.cache.get(key);
if (cached && Date.now() - cached.timestamp < this.ttl) {
return cached.value;
}
const value = await generator();
this.cache.set(key, {
value,
timestamp: Date.now()
});
return value;
}
}
// Usage
const cache = new AICache();
const result = await cache.get(
promptHash,
() => aiService.generate(prompt)
);
Consider:
- Cache invalidation strategies
- Storage options (memory vs. distributed)
- Cache key design
- TTL policies
4. The Fallback Chain Pattern
AI services can fail or become unavailable. Having fallback options ensures your application remains functional.
class FallbackChain {
constructor(services) {
this.services = services;
}
async execute(input) {
for (const service of this.services) {
try {
return await service.generate(input);
} catch (error) {
if (service === this.services[this.services.length - 1]) {
throw error;
}
// Log failure and continue to next service
console.error(`Service failed: ${error.message}`);
}
}
}
}
// Usage
const chain = new FallbackChain([
primaryAIService,
backupAIService,
fallbackRuleEngine
]);
Benefits:
- Improved reliability
- Cost optimization opportunities
- Graceful degradation
- Better user experience
5. The Result Validator Pattern
AI outputs need validation to ensure they meet your application's requirements.
class ResultValidator {
constructor(validators) {
this.validators = validators;
}
async validate(result) {
const issues = [];
for (const validator of this.validators) {
const valid = await validator(result);
if (!valid.success) {
issues.push(valid.error);
}
}
return {
valid: issues.length === 0,
issues
};
}
}
// Usage
const validator = new ResultValidator([
result => ({
success: result.length <= 1000,
error: 'Response too long'
}),
result => ({
success: !containsSensitiveInfo(result),
error: 'Contains sensitive information'
})
]);
Important aspects:
- Input validation
- Output sanitization
- Content safety checks
- Format verification
Implementing These Patterns
While these patterns are powerful, implementing them properly requires careful consideration of:
- Error handling strategies
- Monitoring and logging
- Performance implications
- Testing approaches
The key is to find the right balance between reliability and complexity for your specific use case.
Simplifying Implementation
While understanding these patterns is valuable, implementing them properly requires significant engineering resources. Waveloom's SDK provides these patterns out of the box:
- Chain of Responsibility → Built into our visual workflow builder
- Retry Management → Automatic handling with configurable policies
- Caching → Integrated caching layer with smart invalidation
- Fallbacks → Simple service switching and redundancy
- Validation → Pre-built validators and custom rules support
This means you can focus on building your AI features instead of implementing infrastructure patterns.
Looking Ahead
As AI applications become more complex, these patterns will evolve and new ones will emerge. The most successful teams will be those that can efficiently implement and adapt these patterns while focusing on their core business logic.