When a company says it wants a model that “knows our business,” it is usually describing one of two needs. Either the model must use current, permissioned company knowledge — policies, tickets, product data, contracts — or it must behave in a particular style or domain language. Those are not the same engineering problem.
Retrieval Augmented Generation (RAG) keeps knowledge outside the model. Documents and records are ingested, chunked, embedded and retrieved at question time. The model then answers with that context. When a policy changes, you update the source — you do not retrain the universe. RAG is the default pattern for enterprise knowledge assistants, document Q&A and any system that must cite or stay within approved sources.
Fine-tuning changes the model’s weights. It is useful when you need a consistent format, a specialized language, or behavior that prompting cannot reliably produce. It is a poor way to store facts that change. Weights are not a content management system, and they do not respect document-level permissions on their own.
Many production systems use both, lightly. RAG for knowledge. A small amount of tuning or, more often, good system design — tools, evaluation, prompt contracts — for behavior. The expensive mistake is fine-tuning in the hope that the model will memorize the intranet.
A practical test: if the answer must change when a file changes this week, you need retrieval. If the answer must always look like your case notes or always extract fields in a strict schema, you may need structured outputs, tools, or tuning. If you need both, design the system rather than picking a slogan.
Zynavia typically starts with RAG and application integration because that is how most businesses actually create value with generative AI. Fine-tuning is added when there is evidence it will outperform a simpler, more operable design.
