RAG vs Fine-Tuning

The short version

Default to RAG. For roughly 80% of enterprise "we want AI on our company's data" problems, RAG is the right answer — cheaper, faster to update, easier to audit, and produces citations. Fine-tuning is the right answer when the problem is about style, format, or very low-resource domains — not about factual knowledge.

Most teams that reach for fine-tuning first end up needing RAG anyway. Very few teams that start with RAG end up needing fine-tuning.

What each technique actually does

RAG (Retrieval-Augmented Generation)

The model's weights don't change. At query time, the system retrieves relevant passages from your documents and includes them as context in the prompt. The model answers using that context. Source documents are cited.

Fine-tuning

The model's weights change. You train on pairs of inputs and desired outputs; the model learns to produce outputs that match the training distribution. Knowledge about your specific content is baked into the weights rather than retrieved at query time.

Decision framework

Choose RAG when...

• The task is about factual retrieval ("what does our contract with X say about Y?")
• Your knowledge base changes frequently — new docs, updates, deprecations
• You need citations for auditability, compliance, or user trust
• You care about hallucination control — RAG grounds answers in specific retrieved content
• You want to update the system without retraining (just drop new docs in the index)
• Your data is too large or too private to fit in a training run

Choose fine-tuning when...

• The task is about style — matching a specific voice, tone, or writing pattern
• The task is about format — producing rigidly structured output (specific JSON schema, specific prose pattern) reliably
• You're working in a very low-resource domain (rare language, highly specialized vocabulary) where retrieval alone leaves gaps
• You need very fast inference and can't afford the retrieval step's latency overhead
• You have high-volume, narrow task usage where even small quality gains pay off across millions of inferences

Choose both (hybrid) when...

Fine-tune for style/format, RAG for factual grounding. This combo shows up in customer-support-AI deployments (fine-tuned to match the brand voice, RAG-grounded in product documentation), proposal generation (fine-tuned to match firm voice, RAG-grounded in prior deals), and research synthesis (fine-tuned on analyst-memo format, RAG over the research archive).

Cost comparison

RAG costs

• Setup: engineering time for ingestion + embedding + retrieval + evaluation. Typically 2–6 weeks for a production-grade deployment.
• Per-query runtime: embedding (cheap) + retrieval (cheap) + LLM inference on augmented prompt (the main cost).
• Updates: near-zero. Drop a document into the pipeline; it's searchable minutes later.
• Observability cost: modest — retrieval results are inspectable.

Fine-tuning costs

• Setup: dataset construction is the expensive part. High-quality fine-tuning datasets require human labeling effort measured in person-weeks to person-months.
• Training run: a few hundred dollars to tens of thousands, depending on model size and dataset volume.
• Updates: expensive. New content means a new training run. In practice, most fine-tuned systems drift as the underlying content changes.
• Observability cost: harder — you can't "see" what the model learned; debugging quality regressions is interpretation work.

The most common mistake: fine-tuning for knowledge

A team fine-tunes a model on their internal documentation and discovers the model still hallucinates, still says things that aren't in the docs, still can't be updated when the docs change. This is because fine-tuning is a poor way to encode specific factual content — it blurs the facts into the weights rather than storing them discretely. RAG is the right tool for that job. Fine-tuning the model's voice while retrieving the facts is the architecture that actually works.

What we default to in client engagements

Our default architecture for "AI on our company's data" problems is RAG on a locally-deployed base model. If the client's use case has strong style or format requirements, we layer in fine-tuning — but only after we've shipped a RAG baseline and measured where it's falling short. The order matters: RAG first, fine-tuning only if measurably needed.

See our RAG pipelines service or the document intelligence platform overview for how this lands in production.