Why LLM Integration Matters for Business
Large language models like GPT-4 and Claude aren't research curiosities any more. Businesses are wiring them into customer support, document processing, search, and internal tools to cut costs and improve quality.
This is a short guide to the four main integration patterns, when each one fits, and where we've watched each one fall over.
Pattern 1: Direct API Integration
The simplest pattern — call the LLM API directly with a prompt. Best for simple text generation, classification, or summarisation where general knowledge is enough.
The limitation: the model only knows what it was trained on and can't see your business data. Useful for "rewrite this email" or "summarise this paragraph." Not useful for "what's the status of order #4823."
Pattern 2: RAG (Retrieval-Augmented Generation)
RAG retrieves relevant documents from your knowledge base and includes them in the prompt context. Best for customer support bots, internal Q&A, and documentation search.
This is the most common enterprise pattern because it doesn't need model training and stays accurate as your data changes. Most of the production LLM work we've shipped is some flavour of RAG.
Pattern 3: Fine-Tuning
Adapt a base model to your domain by training it on your own examples. Best for specialised writing styles, domain-specific classification, and consistent output formats.
Don't use fine-tuning if your data changes frequently (RAG is better) or if you need the model to know facts (fine-tuning teaches style, not knowledge). The teams we've seen reach for fine-tuning first usually should have tried RAG first.
Pattern 4: AI Agents
LLMs that can use tools — search the web, query your database, call APIs, write and execute code. Best for complex multi-step workflows where the AI needs to take actions, not just generate text.
Agents are powerful and also the easiest pattern to over-reach with. Start with the narrowest scope that delivers value and expand from there, not the other way round.
Production Considerations
- Latency: Stream responses with
stream: trueso users see tokens as they arrive rather than waiting on the full completion - Cost: Cache common prompts; use smaller models for simple tasks — the bill scales faster than most teams expect
- Safety: Add input validation and output filtering before serving to real users
- Observability: Log prompts, completions, and latency. The first time something goes wrong, you'll want this data sitting there.
One honest caveat: every one of these patterns can hallucinate, time out, or behave unpredictably on edge cases. The work that separates a demo from production is designing for those failure modes from the start — fallbacks, uncertainty checks, human escalation paths. Skipping that is how AI features end up quietly switched off three months after launch.
Talk to us if you want a sanity check on which pattern fits your use case before you start building.