AI in Production: Beyond the Demo

The Prototype Trap

ChatGPT made it look easy. Feed a prompt, get magic. But the gap between "it works on my machine" and "it works for 100K users" is where AI projects die.

We've shipped 12 LLM applications to production. Here's what nobody tells you.

1. Evaluation Is Your Product

You cannot improve what you cannot measure. Before writing a single line of inference code, define:

interface EvaluationCriteria {
  accuracy: { threshold: 0.9; metric: "exact_match" | "f1" | "semantic_similarity" };
  latency: { p50: 2000; p99: 5000 }; // ms
  cost: { maxPerRequest: 0.05; maxDaily: 5000 }; // USD
  safety: { hallucinationRate: 0.01; piiLeakage: 0; promptInjection: 0 };
  ux: { helpfulness: 4.0; tone: "professional" };
}

Automated evaluation pipeline runs on every commit. Human evaluation for subjective criteria weekly.

2. Guardrails Are Not Optional

Production LLMs need layers of protection:

const guardrails = [
  inputValidation({ maxTokens: 4000, blockedPatterns: [PII_REGEX, INJECTION_REGEX] }),
  promptTemplate({ version: "v2.3.0", variables: ["context", "question"] }),
  outputValidation({ 
    schema: ResponseSchema,
    hallucinationCheck: { threshold: 0.8, method: "self_consistency" },
    piiScrubber: true,
    toneCheck: { allowed: ["professional", "helpful"], blocked: ["opinionated", "speculative"] }
  }),
  rateLimit({ tier: "premium", requestsPerMinute: 60 }),
  circuitBreaker({ failureThreshold: 0.1, resetTimeout: 30000 }),
];

Each request passes through the pipeline. Failures return graceful fallbacks, not errors.

3. RAG Is a System, Not a Library

Retrieval-Augmented Generation requires:

• Chunking strategy: Semantic, not fixed-size. We use recursive character splitting with overlap.
• Embedding model: Domain-specific beats general. Fine-tuned BGE for legal, financial, medical.
• Vector DB: Hybrid search (vector + keyword). We use Qdrant with BM25 + dense vectors.
• Reranking: Cross-encoder reranks top-20 to top-5. Critical for precision.
• Citation tracking: Every claim links to source. UI shows citations inline.

4. Cost Architecture

LLM costs scale non-linearly. Our cost model:

const costControls = {
  modelRouting: {
    simple: "gpt-4o-mini",      // $0.15/1M tokens
    complex: "gpt-4o",           // $5/1M tokens
    reasoning: "o1-preview",     // $15/1M tokens
  },
  caching: {
    semanticCache: { threshold: 0.95, ttl: 86400 },
    exactCache: { ttl: 3600 },
  },
  budget: {
    dailyLimit: 10000,
    alertThreshold: 0.8,
    autoDowngrade: true,
  },
};

Semantic caching alone reduces costs 40-60% for repetitive queries.

5. Observability for AI

Traditional metrics don't capture AI quality. We track:

• Token usage: Input/output by model, user, feature
• Latency distribution: TTFT (time to first token), total latency
• Quality signals: User feedback (thumbs up/down), regeneration rate, copy rate
• Safety metrics: Guardrail triggers, false positive/negative rates
• Drift detection: Embedding drift of inputs, output distribution shifts

6. The Human Loop

AI fails. Design for it.

• Escalation to human for low-confidence predictions
• Feedback collection on every response
• Active learning pipeline: human corrections → fine-tuning data
• A/B testing: model versions, prompts, retrieval strategies

The Reality

Production AI is 20% model, 80% engineering.

The companies winning with AI aren't the ones with the best prompts. They're the ones with the best evaluation, guardrails, cost control, and feedback loops.

Exploring AI for your product? Let's discuss.