LLM Integration Patterns That Actually Ship

Production LLM features fail when teams treat the model as the product. The patterns that ship reliably put the model inside a bounded workflow with clear inputs, outputs, and fallbacks.

Below are approaches we use on client projects, each optimised for maintainability, cost control, and predictable behaviour under real user input.

Retrieval-augmented generation (RAG) done minimally

Start with a small, curated knowledge base - FAQs, policy PDFs, product docs - chunked and embedded once. At query time, retrieve top matches and pass them to the model with a strict system prompt: answer only from context, otherwise say you do not know.

Avoid auto-syncing entire wikis on day one. Stale or contradictory chunks cause hallucinations. Version your corpus and log which chunks were used per answer for debugging.

Structured outputs over free-form text

When downstream code consumes model output - CRM updates, ticket routing, form prefill - require JSON schema or tool calls instead of parsing prose. Most providers support function calling or JSON mode; use it.

Validate outputs server-side before side effects. A failed parse should retry with a narrower prompt or route to a human queue, not silently write bad data.

Cost, latency, and observability

Cache embeddings and frequent queries. Route simple classification to smaller models; reserve large models for synthesis steps. Set per-user and per-tenant rate limits from day one.

Log prompts, token usage, latency, and user feedback (thumbs up/down) tied to session IDs. Without traces, tuning production behaviour is guesswork.