LLM Integration Patterns Every Enterprise Architect Should Know
RAG, agents, fine-tuning, prompt chains — there's a lot of noise. Here's a clear framework for picking the right pattern for your use case.
Every week a new framework, a new model, a new paper claiming to solve reasoning. For enterprise architects trying to make durable decisions, the noise is exhausting. Here's the framework we use at CogniQubit to cut through it.
The four integration patterns
1. Prompt engineering (zero/few-shot)
Best for: well-defined, bounded tasks with predictable inputs. Classification, extraction, reformatting, summarisation. Underrated and often sufficient. If you can solve your problem with a good prompt, don't complicate it.
2. RAG (Retrieval-Augmented Generation)
Best for: Q&A over your proprietary knowledge base. Documents, manuals, policies, code — anything that doesn't fit in a context window and changes frequently. RAG avoids the cost and staleness of fine-tuning by keeping knowledge in a retrieval layer (vector DB) and fetching it at inference time.
The failure mode: treating RAG as a simple "plug in a vector DB" exercise. The hard work is chunking strategy, embedding model selection, re-ranking, and query reformulation. These are the dials that determine whether your system gives useful answers or confident hallucinations.
3. Agentic systems
Best for: multi-step tasks that require tool use, planning, or feedback loops. Think: "research this company and draft a personalised outreach email" or "write and run code until the tests pass."
Agents are powerful and brittle. The key engineering challenge is failure recovery — what happens when a tool call fails or returns unexpected output? Production-grade agents need explicit fallback logic and human-in-the-loop checkpoints for consequential actions.
4. Fine-tuning
Best for: consistent style/format adherence, domain-specific vocabulary, reducing prompt length at scale. Fine-tuning is often the last resort, not the first. It's expensive, requires labelled data, and makes your model stale the moment the base model improves.
How to choose
We use a simple decision flow: start with prompting, add RAG if you need external knowledge, add tool use if you need actions, add fine-tuning only if you've exhausted the others and have volume to justify it.
Want to map this to your specific use case? Book a 30-minute architecture review.