LLM Integration Patterns for the Enterprise

Integrating large language models into enterprise systems requires more than API calls. Here are the patterns we've found most effective when building reliable, production-grade AI features.

The Prompt Engineering Trap

Many teams start by writing increasingly complex prompts. This works for demos but breaks down in production. Instead, we structure LLM interactions as composable stages:

User Input
  → Intent Classification (small, fast model)
  → Context Retrieval (RAG pipeline)
  → Response Generation (capable model with retrieved context)
  → Output Validation (rules + lightweight model)
  → Final Response

Each stage has clear inputs, outputs, and failure modes. This is far more maintainable than a single monolithic prompt.

Pattern 1: Structured Output

LLMs generate text, but downstream systems need structured data. Always constrain outputs to a defined schema:

{
  "intent": "refund_request",
  "confidence": 0.94,
  "entities": {
    "order_id": "ORD-2026-4821",
    "reason": "defective_product",
    "preferred_resolution": "full_refund"
  },
  "requires_human_review": false
}

Use schema validation on every LLM response. When the output doesn't conform, retry with a correction prompt or fall back to a default handler.

Pattern 2: Retrieval-Augmented Generation (RAG)

RAG grounds LLM responses in your actual data rather than relying on the model's training knowledge:

Component	Purpose	Key Consideration
Document Store	Source of truth	Keep up to date
Embedding Model	Semantic indexing	Match to your domain
Vector Database	Fast similarity search	Tune top-K and thresholds
Reranker	Precision filtering	Improves relevance significantly
Generator	Final answer	Include source citations

The most common RAG failure is retrieving irrelevant context. Invest heavily in chunking, embedding quality, and reranking.

Pattern 3: Guardrails and Safety

Every LLM integration needs boundaries:

• Input filtering — Block prompt injection attempts and out-of-scope queries
• Output validation — Check for PII leakage, policy violations, and hallucinated facts
• Rate limiting — Protect against cost overruns and abuse
• Fallback paths — Graceful degradation when the model is unavailable or uncertain

These aren't optional for enterprise deployments. They're the difference between a demo and a system you can trust.

Pattern 4: Model Routing

Not every query needs your most expensive model. Route based on complexity:

Simple FAQ          → Small model (fast, cheap)
Document summary    → Mid-tier model (balanced)
Complex analysis    → Large model (capable, slower)
Ambiguous/risky     → Human review queue

This can cut inference costs by 60-80% while maintaining quality where it matters.

The Bottom Line

LLM integration is systems engineering, not magic. The patterns that work are the same ones that have always worked in distributed systems: clear contracts, graceful failure handling, observability, and incremental complexity.