Definitions without the TED talk

RAG: fetch relevant chunks from your knowledge sources, stuff them into context, ask the model to answer with citations or traceability. Fine-tuning: adjust model weights using curated examples so the model internalizes patterns — tone, formatting, specialized jargon.

Neither fixes bad product definition — they accelerate good definitions.

Decision matrix — what actually drives the call

Use this like a pre-flight checklist — arguments at standup take longer than scoring rows honestly.

When RAG wins

Internal knowledge bases, customer support grounded in docs, sales enablement where answers must track product releases — any domain where truth updates faster than your release calendar.

RAG done poorly looks like keyword search glued to ChatGPT — done well it includes reranking, evaluation on fresh documents, and policies for when to refuse.

When fine-tuning wins

You need consistent structured outputs that parsing pipelines rely on — fine-tune after you have examples, not hopes.

Specialized vocabulary (legal, clinical research assistants with constraints) can be cheaper to encode via adapters than by stuffing giant prompts — but you still compliance-review outputs.

Hybrid — what we deploy most often

RAG for facts, light LoRA-style adaptation for voice or specialist formatting — keep adapters versioned like any other artifact.

Train stakeholders that fine-tuning is not “set and forget” — track drift the same way you track dependency upgrades.

Cost intuition (same dataset story)

RAG spend trends with storage, embedding jobs, and query volume — predictable if you cache embeddings and dedupe documents.

Fine-tuning spend trends with GPU training hours and specialist time labeling — predictable if your dataset is frozen; expensive if it is not.

Frequently asked questions