A PaperBridge long-tail question
Should I use RAG or fine-tuning?
If the failure is “the model lacks current or private facts,” start with RAG. If it knows the information but repeatedly behaves incorrectly, consider fine-tuning. RAG is easier to update, remove, permission, and cite. Fine-tuning is better for stable format, tone, classification boundaries, and task behavior. Many production systems combine them: retrieval supplies facts and fine-tuning shapes how those facts are used.
Changing, permissioned, or cited knowledge: prefer RAG
Documentation, policy, pricing, inventory, and internal knowledge change. A retrieval layer is easier to update and revoke than repeated model training.
RAG also records which documents were retrieved. It does not eliminate hallucination: bad retrieval, poor chunking, and failure to follow evidence remain possible.
Stable behavior: consider fine-tuning
Fixed JSON, brand tone, classification policy, domain terminology, and repeatable task steps are behavior-learning problems. Reviewed examples can change response habits through full or parameter-efficient fine-tuning.
Do not treat fine-tuning as a database. Parameterized facts are difficult to update individually, revoke, or cite reliably.
Build the cheapest baseline before training
Start with a clear prompt and a few examples. If prompting plus output validation is sufficient, stop there. Add retrieval for missing facts; collect failure cases for fine-tuning only if behavior remains unstable.
Evaluate retrieval and generation separately: did the system fetch the right evidence, and did the model use that evidence faithfully?
A common combination: RAG for knowledge, tuning for behavior
A support assistant might retrieve the current returns policy while a fine-tuned behavior enforces the question sequence and ticket format. The methods solve different layers.
With little data, delay fine-tuning. A dozen carefully chosen prompt examples can be safer than hundreds of repetitive or contradictory training examples.
Five-question decision check
- Does the knowledge change weekly or monthly? Prefer RAG.
- Must answers expose sources or enforce permissions? Prefer RAG.
- Is the main failure format, tone, or process inconsistency? Consider fine-tuning.
- Do you have many reviewed input-output examples? Fine-tuning becomes more credible.
- Do you need both current facts and stable behavior? Combine them.
Primary research and official documentation
These sources support the facts. Workflow and comparison guidance is PaperBridge's synthesis of research, official documentation, and engineering practice.