Retrieval-augmented generation (RAG) and fine-tuning are often discussed as competing approaches. In practice they solve different problems and frequently belong in the same system.
What RAG is good at
RAG injects fresh, governed knowledge into the prompt at inference time. It is the default choice when content changes frequently, citations are required, or access controls matter.
What fine-tuning is good at
Fine-tuning bakes patterns into the model — tone, structured output formats, domain vocabulary. It is the right tool for narrow, high-volume tasks where prompt length is a cost driver.
How we combine them
- Fine-tune for output shape and domain tone.
- Retrieve for ground truth, policy text, and product data.
- Evaluate with golden sets that mirror real user prompts.
The single most common failure mode we see is shipping a RAG system without an evaluation harness. Without measurable accuracy on the prompts that actually matter, the program drifts into anecdote.
About the author. This article was written by the consulting team at Algorithm, Inc, a U.S.-based software development and digital transformation firm headquartered in Dublin, Ohio. To discuss how these ideas apply to your environment, contact us.