The API-First AI Strategy: A Software Architect's Guide to Building with LLM APIs

Your CTO asks: "Can we use AI to automate our support tickets?"

You investigate. OpenAI API looks promising. You write a quick prototype. It works. You show the demo. Everyone's excited. Then you start thinking about production:

• How do we handle rate limits at scale?
• What happens when the API is down?
• How do we control costs when traffic spikes?
• Where does this fit in our architecture?
• How do we test AI-powered features?
• What about data privacy and compliance?

This is where most teams stall. They treat LLM APIs like any other REST API and run into walls—unpredictable costs, brittleness, and architectural mess.

This guide is for architects and tech leads who need to integrate AI into production systems without creating technical debt. We'll cover architecture patterns, failure modes, cost control, and the trade-offs that matter.

The Problem with "Just Call the API"

Most teams approach LLM integration like this:

// Support ticket handler
async function respondToTicket(ticket: Ticket): Promise<string> {
  const response = await openai.chat.completions.create({
    model: "gpt-4",
    messages: [{ role: "user", content: ticket.content }]
  });
  
  return response.choices[0].message.content;
}

This works in demos. It fails in production.

Problems:

1. Cost explosion

   • GPT-4 costs $0.03 per 1K input tokens, $0.06 per 1K output tokens
   • One ticket averages 500 input + 800 output tokens
   • 10,000 tickets/day = $720/day = $21,600/month
   • Spikes during incidents can cost thousands in hours

2. Reliability issues

   • No retry logic → failures bubble up
   • No fallback → system unusable during API outages
   • No rate limiting → getting throttled, requests fail

3. Quality problems

   • No validation → hallucinations reach customers
   • No context → generic responses
   • No consistency → different answers for same question

4. Architecture debt

   • AI logic scattered across codebase
   • No testing strategy
   • No observability (costs, latency, failure rate)
   • Tight coupling to vendor

The fix: Treat AI as a strategic architectural component, not just "another API call."

The Architecture Patterns You Need

Pattern 1: AI Gateway (Abstraction Layer)

Problem: Direct coupling to OpenAI, Anthropic, or other providers.

Solution: Create an abstraction that isolates AI vendors.

// Domain interface (vendor-agnostic)
interface AIProvider {
  complete(prompt: CompletionRequest): Promise<CompletionResponse>;
  embed(text: string): Promise<number[]>;
  moderate(content: string): Promise<ModerationResult>;
}

// Concrete implementations
class OpenAIProvider implements AIProvider {
  async complete(request: CompletionRequest): Promise<CompletionResponse> {
    // OpenAI-specific logic
    const response = await this.client.chat.completions.create({
      model: request.model || 'gpt-4',
      messages: this.formatMessages(request.messages),
      temperature: request.temperature,
      max_tokens: request.maxTokens
    });
    
    return this.mapResponse(response);
  }
}

class AnthropicProvider implements AIProvider {
  async complete(request: CompletionRequest): Promise<CompletionResponse> {
    // Anthropic-specific logic
    // Different API, same interface
  }
}

// Usage in application layer
class TicketResponseUseCase {
  constructor(private aiProvider: AIProvider) {}
  
  async execute(ticket: Ticket): Promise<Result<string, DomainError>> {
    const response = await this.aiProvider.complete({
      messages: [
        { role: 'system', content: this.getSystemPrompt() },
        { role: 'user', content: ticket.content }
      ],
      temperature: 0.7,
      maxTokens: 500
    });
    
    return ok(response.content);
  }
}

Benefits:

• ✅ Switch providers without changing business logic
• ✅ A/B test multiple providers
• ✅ Fallback to cheaper models during cost spikes
• ✅ Mock for testing

Pattern 2: Prompt Management (Version Control for Prompts)

Problem: Prompts hardcoded in application code. Changes require deployments. No versioning or A/B testing.

Solution: Treat prompts as configuration, not code.

Database Schema:

CREATE TABLE prompts (
  id UUID PRIMARY KEY,
  key VARCHAR(255) UNIQUE NOT NULL,
  version INTEGER NOT NULL,
  content TEXT NOT NULL,
  variables JSONB,
  model VARCHAR(50),
  temperature FLOAT,
  max_tokens INTEGER,
  active BOOLEAN DEFAULT false,
  created_at TIMESTAMP,
  created_by VARCHAR(255),
  metadata JSONB
);

CREATE TABLE prompt_metrics (
  id UUID PRIMARY KEY,
  prompt_id UUID REFERENCES prompts(id),
  timestamp TIMESTAMP,
  tokens_used INTEGER,
  latency_ms INTEGER,
  cost_usd DECIMAL(10, 6),
  user_rating INTEGER,
  hallucination_detected BOOLEAN
);

Usage:

class PromptManager {
  async getPrompt(key: string, variables: Record<string, any>): Promise<PromptConfig> {
    // Get active version from database
    const prompt = await this.repository.findActiveByKey(key);
    
    // Interpolate variables
    const content = this.interpolate(prompt.content, variables);
    
    // Track usage
    await this.metrics.record({
      promptId: prompt.id,
      version: prompt.version,
      timestamp: new Date()
    });
    
    return {
      content,
      model: prompt.model,
      temperature: prompt.temperature,
      maxTokens: prompt.maxTokens
    };
  }
  
  private interpolate(template: string, vars: Record<string, any>): string {
    return template.replace(/\{\{(\w+)\}\}/g, (_, key) => vars[key] || '');
  }
}

// Application code
const promptConfig = await promptManager.getPrompt('support-ticket-response', {
  ticketCategory: ticket.category,
  customerTier: ticket.customer.tier,
  previousResponses: ticket.history.length
});

const response = await aiProvider.complete({
  messages: [
    { role: 'system', content: promptConfig.content },
    { role: 'user', content: ticket.content }
  ],
  model: promptConfig.model,
  temperature: promptConfig.temperature
});

Benefits:

• ✅ Update prompts without deployment
• ✅ A/B test prompt variations
• ✅ Version control and rollback
• ✅ Track which prompts perform best
• ✅ Non-engineers can iterate on prompts

Pattern 3: Response Validation (Guard Against Hallucinations)

Problem: LLMs hallucinate. You can't send unvalidated responses to customers.

Solution: Multi-layer validation pipeline.

interface ValidationRule {
  validate(response: string, context: any): ValidationResult;
}

class ResponseValidator {
  constructor(private rules: ValidationRule[]) {}
  
  async validate(
    response: string,
    context: ValidationContext
  ): Promise<ValidationResult> {
    for (const rule of this.rules) {
      const result = await rule.validate(response, context);
      if (!result.isValid) {
        return result;
      }
    }
    return { isValid: true };
  }
}

// Validation rules
class NoHallucinatedFactsRule implements ValidationRule {
  async validate(response: string, context: any): Promise<ValidationResult> {
    // Check if response mentions facts not in source material
    const facts = this.extractFacts(response);
    const sourceFacts = this.extractFacts(context.source);
    
    const hallucinated = facts.filter(f => !sourceFacts.includes(f));
    
    if (hallucinated.length > 0) {
      return {
        isValid: false,
        reason: 'Response contains facts not in source',
        details: hallucinated
      };
    }
    
    return { isValid: true };
  }
}

class NoInappropriateContentRule implements ValidationRule {
  async validate(response: string): Promise<ValidationResult> {
    // Use OpenAI moderation API
    const moderation = await this.aiProvider.moderate(response);
    
    if (moderation.flagged) {
      return {
        isValid: false,
        reason: 'Content flagged by moderation API',
        details: moderation.categories
      };
    }
    
    return { isValid: true };
  }
}

class CorrectSchemaRule implements ValidationRule {
  async validate(response: string, context: any): Promise<ValidationResult> {
    try {
      const parsed = JSON.parse(response);
      const validation = context.schema.safeParse(parsed);
      
      if (!validation.success) {
        return {
          isValid: false,
          reason: 'Response does not match expected schema',
          details: validation.error
        };
      }
      
      return { isValid: true };
    } catch (error) {
      return {
        isValid: false,
        reason: 'Response is not valid JSON'
      };
    }
  }
}

// Usage
const validator = new ResponseValidator([
  new NoHallucinatedFactsRule(),
  new NoInappropriateContentRule(),
  new CorrectSchemaRule()
]);

const validationResult = await validator.validate(aiResponse, {
  source: ticket.context,
  schema: ResponseSchema
});

if (!validationResult.isValid) {
  // Retry with different prompt or fallback to human
  await this.handleValidationFailure(validationResult);
}

Benefits:

• ✅ Catch hallucinations before they reach customers
• ✅ Enforce response structure
• ✅ Maintain brand safety
• ✅ Reduce liability risk

Pattern 4: Cost Control (Rate Limiting & Budgets)

Problem: AI costs are unpredictable and can spike uncontrollably.

Solution: Implement cost controls at multiple levels.

class CostController {
  constructor(
    private budgetManager: BudgetManager,
    private rateLimiter: RateLimiter,
    private costPredictor: CostPredictor
  ) {}
  
  async checkAndReserve(
    request: AIRequest,
    context: CostContext
  ): Promise<Result<CostReservation, CostError>> {
    // 1. Predict cost
    const estimatedCost = this.costPredictor.estimate(request);
    
    // 2. Check rate limits (requests per minute/hour)
    const rateLimitResult = await this.rateLimiter.checkLimit(
      context.userId,
      context.feature
    );
    if (!rateLimitResult.allowed) {
      return err(new RateLimitExceededError());
    }
    
    // 3. Check budget (daily/monthly caps)
    const budgetResult = await this.budgetManager.checkBudget(
      context.feature,
      estimatedCost
    );
    if (!budgetResult.available) {
      return err(new BudgetExceededError());
    }
    
    // 4. Reserve budget
    const reservation = await this.budgetManager.reserve(
      context.feature,
      estimatedCost
    );
    
    return ok(reservation);
  }
  
  async recordActual(
    reservation: CostReservation,
    actualCost: number
  ): Promise<void> {
    // Adjust budget based on actual cost
    const diff = actualCost - reservation.estimatedCost;
    await this.budgetManager.adjust(reservation.id, diff);
    
    // Record metrics
    await this.metrics.record({
      feature: reservation.feature,
      estimatedCost: reservation.estimatedCost,
      actualCost,
      accuracy: 1 - Math.abs(diff) / reservation.estimatedCost
    });
  }
}

// Budget configuration
interface BudgetConfig {
  feature: string;
  dailyLimit: number;    // USD
  monthlyLimit: number;  // USD
  alertThreshold: number; // 0.8 = 80%
  fallbackStrategy: 'queue' | 'cheaper-model' | 'reject';
}

// Usage
const costResult = await costController.checkAndReserve(request, {
  userId: ticket.userId,
  feature: 'support-ticket-response'
});

if (costResult.isErr()) {
  // Budget exceeded, fallback strategy
  if (config.fallbackStrategy === 'cheaper-model') {
    request.model = 'gpt-3.5-turbo'; // Cheaper alternative
  } else if (config.fallbackStrategy === 'queue') {
    await this.queue.add(request); // Process later
    return ok('Request queued due to budget limits');
  }
}

const response = await aiProvider.complete(request);

// Record actual cost
await costController.recordActual(
  costResult.value,
  response.usage.totalCost
);

Cost Optimization Strategies:

1. Tiered Models

   • Use GPT-4 for complex/high-value requests
   • Use GPT-3.5 for simple/bulk requests
   • Use local models for classification tasks

2. Caching

   • Cache responses for identical requests
   • Use semantic caching (similar requests)
   • Cache expensive embeddings

3. Prompt Optimization

   • Shorter prompts = lower costs
   • Remove unnecessary context
   • Use prompt compression techniques

4. Batch Processing

   • Batch similar requests together
   • Process during off-peak hours
   • Use cheaper models for batch jobs

Pattern 5: Observability (Know What's Happening)

Problem: You can't improve what you can't measure.

Solution: Instrument everything.

class AIObservability {
  async recordRequest(event: AIRequestEvent): Promise<void> {
    await this.metrics.record({
      // Performance metrics
      latency: event.latency,
      tokenCount: event.usage.totalTokens,
      
      // Cost metrics
      costUSD: event.usage.totalCost,
      
      // Quality metrics
      validationPassed: event.validation.passed,
      validationFailureReason: event.validation.failureReason,
      
      // User feedback (if available)
      userRating: event.feedback?.rating,
      userReported: event.feedback?.reported,
      
      // Context
      model: event.request.model,
      promptVersion: event.promptVersion,
      feature: event.feature,
      timestamp: event.timestamp
    });
  }
  
  // Dashboards
  async getCostBreakdown(timeRange: TimeRange): Promise<CostBreakdown> {
    return {
      byFeature: await this.metrics.groupBy('feature', 'costUSD', timeRange),
      byModel: await this.metrics.groupBy('model', 'costUSD', timeRange),
      byPromptVersion: await this.metrics.groupBy('promptVersion', 'costUSD', timeRange),
      total: await this.metrics.sum('costUSD', timeRange)
    };
  }
  
  async getQualityMetrics(timeRange: TimeRange): Promise<QualityMetrics> {
    return {
      validationPassRate: await this.metrics.rate('validationPassed', timeRange),
      averageUserRating: await this.metrics.avg('userRating', timeRange),
      hallucinationRate: await this.metrics.rate('hallucination', timeRange),
      reportRate: await this.metrics.rate('userReported', timeRange)
    };
  }
  
  async getPerformanceMetrics(timeRange: TimeRange): Promise<PerformanceMetrics> {
    return {
      p50Latency: await this.metrics.percentile('latency', 0.50, timeRange),
      p95Latency: await this.metrics.percentile('latency', 0.95, timeRange),
      p99Latency: await this.metrics.percentile('latency', 0.99, timeRange),
      avgTokensPerRequest: await this.metrics.avg('tokenCount', timeRange)
    };
  }
}

// Alert rules
class AIAlertManager {
  rules: AlertRule[] = [
    {
      name: 'High cost spike',
      condition: (metrics) => metrics.hourlySpend > metrics.avgHourlySpend * 2,
      action: async () => {
        await this.notify('Cost spike detected, switching to cheaper models');
        await this.configManager.update({ defaultModel: 'gpt-3.5-turbo' });
      }
    },
    {
      name: 'High validation failure rate',
      condition: (metrics) => metrics.validationFailureRate > 0.15,
      action: async () => {
        await this.notify('High validation failure rate, check prompt quality');
        await this.disablePrompt(metrics.promptVersion);
      }
    },
    {
      name: 'High latency',
      condition: (metrics) => metrics.p95Latency > 5000,
      action: async () => {
        await this.notify('High latency detected');
        await this.scaleUp('ai-service');
      }
    }
  ];
}

Key Metrics to Track:

Architecture Decision Framework

When integrating AI into your system, answer these questions:

1. Synchronous vs. Asynchronous?

Synchronous (User waits for response):

• ✅ Best for: Chat interfaces, real-time suggestions
• ❌ Risks: High latency (2-10s), timeout issues
• 💡 Mitigation: Use streaming responses, show progress

Asynchronous (Background processing):

• ✅ Best for: Document analysis, batch operations
• ❌ Risks: Complexity, state management
• 💡 Mitigation: Use job queues, WebSocket for updates

Decision Matrix:

2. Which Model(s) to Use?

Model Selection Matrix:

Multi-Model Strategy:

class ModelSelector {
  selectModel(request: AIRequest): string {
    // High-value users get best model
    if (request.user.tier === 'enterprise') {
      return 'gpt-4';
    }
    
    // Complex tasks need powerful models
    if (request.complexity === 'high') {
      return 'gpt-4-turbo';
    }
    
    // Simple tasks use cheaper models
    if (request.complexity === 'low') {
      return 'gpt-3.5-turbo';
    }
    
    // Default
    return 'gpt-3.5-turbo';
  }
}

3. Where Does AI Fit in Your Architecture?

Option A: Direct Integration (Simple)

Controller → AI Provider → Response

✅ Pros: Simple, fast to implement
❌ Cons: Tight coupling, hard to test, no observability

Use when: Prototyping, low-volume features

Option B: Service Layer (Recommended)

Controller → Use Case → AI Service → AI Provider → Response
                     ↓
                Validation
                     ↓
                 Caching
                     ↓
               Observability

✅ Pros: Testable, observable, reusable
❌ Cons: More code, slightly more complex

Use when: Production systems, multiple features using AI

Option C: Event-Driven (Complex)

Event Bus → AI Worker → Process → Store → Notify User

✅ Pros: Scalable, decoupled, resilient
❌ Cons: Complex, eventual consistency

Use when: High volume, long-running tasks, need to scale

4. How to Handle Failures?

Failure Modes:

1. API Timeout (LLM took too long)

   • Mitigation: Set reasonable timeouts (30s), retry with backoff

2. API Rate Limit (Too many requests)

   • Mitigation: Implement client-side rate limiting, queue requests

3. API Error (500, 503)

   • Mitigation: Retry with exponential backoff, circuit breaker

4. Validation Failure (Hallucination detected)

   • Mitigation: Retry with different prompt, escalate to human

5. Cost Limit Reached

   • Mitigation: Queue request, use cheaper model, reject gracefully

Resilience Pattern:

class ResilientAIService {
  async complete(
    request: AIRequest,
    options: ResilienceOptions = {}
  ): Promise<Result<AIResponse, AIError>> {
    const {
      maxRetries = 3,
      retryDelay = 1000,
      timeout = 30000,
      fallbackModel = 'gpt-3.5-turbo'
    } = options;
    
    let lastError: AIError | null = null;
    
    for (let attempt = 1; attempt <= maxRetries; attempt++) {
      try {
        // Check circuit breaker
        if (!this.circuitBreaker.isAvailable(request.provider)) {
          return err(new ServiceUnavailableError());
        }
        
        // Execute with timeout
        const response = await this.executeWithTimeout(request, timeout);
        
        // Validate response
        const validation = await this.validator.validate(response);
        if (!validation.isValid) {
          lastError = new ValidationError(validation.reason);
          continue; // Retry
        }
        
        // Success
        this.circuitBreaker.recordSuccess(request.provider);
        return ok(response);
        
      } catch (error) {
        lastError = this.mapError(error);
        
        // Record failure for circuit breaker
        this.circuitBreaker.recordFailure(request.provider);
        
        // Rate limit or unrecoverable error → don't retry
        if (error instanceof RateLimitError || error instanceof AuthError) {
          return err(lastError);
        }
        
        // Retry with backoff
        if (attempt < maxRetries) {
          await this.sleep(retryDelay * Math.pow(2, attempt - 1));
        }
      }
    }
    
    // All retries failed, try fallback model
    if (fallbackModel && request.model !== fallbackModel) {
      return this.complete({ ...request, model: fallbackModel }, {
        ...options,
        fallbackModel: undefined // Prevent infinite fallback
      });
    }
    
    return err(lastError || new UnknownError());
  }
}

Testing Strategy for AI Features

Challenge: LLMs are non-deterministic. Same input can produce different outputs.

Level 1: Unit Tests (Mock AI)

describe('SupportTicketResponder', () => {
  it('should format response correctly when AI returns valid data', async () => {
    // Given
    const mockAI = {
      complete: jest.fn().mockResolvedValue({
        content: 'Thank you for your question...',
        usage: { totalTokens: 150 }
      })
    };
    const responder = new SupportTicketResponder(mockAI);
    
    // When
    const result = await responder.respond(ticket);
    
    // Then
    expect(result.isOk()).toBe(true);
    expect(result.value).toContain('Thank you');
    expect(mockAI.complete).toHaveBeenCalledWith(
      expect.objectContaining({
        messages: expect.arrayContaining([
          expect.objectContaining({ role: 'system' })
        ])
      })
    );
  });
});

Level 2: Integration Tests (Real AI, Assertions on Structure)

describe('SupportTicketResponder (integration)', () => {
  it('should generate response with correct structure', async () => {
    // Given
    const realAI = new OpenAIProvider(config);
    const responder = new SupportTicketResponder(realAI);
    const ticket = createTestTicket({
      content: 'How do I reset my password?'
    });
    
    // When
    const result = await responder.respond(ticket);
    
    // Then
    expect(result.isOk()).toBe(true);
    const response = result.value;
    
    // Assert structure, not exact content
    expect(response).toHaveProperty('content');
    expect(response.content.length).toBeGreaterThan(50);
    expect(response.content).not.toContain('ERROR');
    expect(response.content).not.toContain('As an AI');
    
    // Assert mentions key terms
    expect(response.content.toLowerCase()).toContain('password');
    expect(response.content.toLowerCase()).toMatch(/reset|change|update/);
  });
});

Level 3: Evaluation Tests (LLM as Judge)

describe('SupportTicketResponder (evaluation)', () => {
  it('should generate helpful and accurate responses', async () => {
    // Given
    const testCases = [
      {
        ticket: 'How do I reset my password?',
        expectedTopics: ['password', 'reset', 'account'],
        expectedTone: 'helpful'
      },
      // ... more test cases
    ];
    
    for (const testCase of testCases) {
      // When
      const response = await responder.respond(testCase.ticket);
      
      // Then - Use LLM as judge
      const evaluation = await evaluator.evaluate({
        response: response.value.content,
        criteria: {
          relevance: 'Does the response address the user question?',
          accuracy: 'Is the information factually correct?',
          helpfulness: 'Would this help the user solve their problem?',
          tone: `Is the tone ${testCase.expectedTone}?`
        },
        context: {
          question: testCase.ticket,
          expectedTopics: testCase.expectedTopics
        }
      });
      
      expect(evaluation.relevance.score).toBeGreaterThan(0.8);
      expect(evaluation.accuracy.score).toBeGreaterThan(0.8);
      expect(evaluation.helpfulness.score).toBeGreaterThan(0.7);
    }
  });
});

Level 4: Regression Tests (Golden Set)

Maintain a set of "golden" examples:

// golden-responses.json
[
  {
    "id": "password-reset-001",
    "input": "How do I reset my password?",
    "expectedResponse": "To reset your password, click...",
    "minSimilarity": 0.85
  }
]

// Test runner
describe('Regression tests', () => {
  goldenExamples.forEach(example => {
    it(`should generate response similar to golden for: ${example.id}`, async () => {
      // When
      const response = await responder.respond(example.input);
      
      // Then - Compare semantic similarity
      const similarity = await semanticSimilarity(
        response.value.content,
        example.expectedResponse
      );
      
      expect(similarity).toBeGreaterThan(example.minSimilarity);
    });
  });
});

Production Checklist

Before going live with AI features:

Cost Controls

• Daily/monthly budget limits configured
• Rate limiting implemented (per user, per feature)
• Cost estimation and alerting in place
• Fallback to cheaper models configured
• Cost dashboard built

Quality Gates

• Response validation pipeline implemented
• Moderation API integrated (if user-facing)
• Hallucination detection rules defined
• Human review process for edge cases
• User feedback collection mechanism

Reliability

• Retry logic with exponential backoff
• Circuit breaker implemented
• Timeout configuration tuned
• Fallback strategies defined
• Health check endpoints created

Observability

• Metrics collection (cost, latency, quality)
• Dashboards for monitoring
• Alert rules configured
• Log aggregation set up
• Cost attribution by feature/user

Security & Compliance

• API keys stored securely (vault, not env vars)
• PII detection and scrubbing
• Data retention policy defined
• Audit logging implemented
• Privacy policy updated

Testing

• Unit tests with mocked AI
• Integration tests with real API
• Evaluation tests (LLM as judge)
• Regression tests (golden set)
• Load testing completed

Real-World Trade-Offs

Trade-Off 1: Latency vs. Cost

Scenario: Support ticket auto-response

Options:

Decision: Use GPT-3.5 for first response, escalate to GPT-4 if user rates it poorly.

Trade-Off 2: Accuracy vs. Speed

Scenario: Code review comments

Options:

1. Single LLM call (fast, less accurate)

   • One prompt with all context
   • Returns all feedback at once
   • Latency: 5-10s
   • May miss subtle issues

2. Multi-step pipeline (slow, more accurate)

   • Step 1: Identify potential issues (fast model)
   • Step 2: Deep analysis of flagged issues (slow model)
   • Step 3: Generate suggestions
   • Latency: 20-30s
   • Higher accuracy

Decision: Use single call for draft feedback, multi-step for final review.

Trade-Off 3: Flexibility vs. Control

Scenario: Content generation

Options:

1. High temperature (0.9): Creative, varied, unpredictable
2. Medium temperature (0.7): Balanced
3. Low temperature (0.3): Consistent, safe, boring

Decision Matrix:

Key Takeaways

1. Treat AI as a strategic component, not "just an API" – requires architecture patterns, observability, cost controls

2. Implement the 5 core patterns:

   • AI Gateway (abstraction)
   • Prompt Management (versioning)
   • Response Validation (quality gates)
   • Cost Control (budgets & rate limits)
   • Observability (metrics & alerts)

3. Design for failure – APIs will be slow, rate-limited, or down; have fallback strategies

4. Control costs aggressively – AI costs can explode; implement budgets, use tiered models, optimize prompts

5. Test differently – LLMs are non-deterministic; test structure and semantic meaning, not exact matches

6. Make trade-offs explicit – latency vs. cost, accuracy vs. speed, flexibility vs. control

7. Build incrementally – start simple (synchronous, single model), add complexity as needed (async, multi-model, validation pipeline)

The teams that succeed with AI in 2026 are those who treat it as a first-class architectural component with proper abstraction, observability, and controls—not as "just another API call."

Your move.