RUBIKSCORPAI systems
RAG vs fine-tuning: how to choose for business AI
RAG supplies retrieved information when a system answers. Fine-tuning changes model parameters through additional training. They address different needs and can be combined, but neither replaces good data, access controls or evaluation.
Knowledge supplied at answer time or behavior adapted through training
A retrieval-augmented system finds relevant material and supplies it as context for generation. A fine-tuned model has undergone an additional training process using examples. Updating a document collection and changing a model’s parameters are different operational tasks.
The original RAG paper combined retrieval with generation for knowledge-intensive tasks. It is foundational research, not a guarantee that a particular document assistant will find or use the right source.
| Need | First approach to test | What still needs checking |
|---|---|---|
| Use frequently updated policies | Retrieval with versioned, approved documents | Freshness, access permissions and source selection |
| Produce a consistent response format | Instructions, examples and schema validation | Field correctness and handling of missing values |
| Improve a repeated specialized behavior | Fine-tuning after a measured baseline | Training quality, held-out performance and maintenance |
| Combine company knowledge with a specialist response style | Retrieval plus an adapted model if justified | Both retrieval and behavior, separately and together |
Worked example: a changing product catalogue
Imagine a supplier whose product dimensions change after a design revision. A document assistant should select the current approved revision and identify it in the answer. A process for publishing, retiring and retrieving those documents is essential.
Training on an old catalogue does not create a live connection to the new catalogue. Retrieval can make approved updates available without retraining, but only if indexing and version selection work. The answer also needs to distinguish between a source saying a dimension and a model inferring one.
If the same assistant repeatedly ignores an approved response format, first test clearer instructions, examples and deterministic validation. Fine-tuning becomes a candidate only after the remaining behavior is described and measurable.
Diagnose retrieval before changing the generator
Keep retrieval tests separate from answer tests. If the right passage never reaches the model, changing tone or model size may conceal the underlying problem rather than fix it.
- Can the correct passage be retrieved for the question?
- Is the latest approved revision identifiable?
- Does the retrieved material belong to this user or account?
- Are tables, units and section boundaries preserved?
- What happens when no source answers the question?
- Do quoted passages actually support the answer?
What a fine-tuning experiment needs
Use examples with a consistent target behavior and remove contradictory labels. Reserve evaluation material that is not used for training or routine prompt adjustment. Closely related records can leak information across splits, so choose the split to reflect the intended deployment.
Record the base model, training data version, adaptation settings and evaluation results. Include the unmodified model as a baseline. Revisit the experiment when the task, source material or provider changes.
Provider support, data-handling terms and pricing vary. Check the current documentation for the selected platform before assuming that a particular fine-tuning method is available.
Make the decision with a small comparison
Select a representative set of questions and agree what a correct answer requires. Compare a simple baseline, retrieval and any proposed adaptation on the same cases. Inspect failures such as obsolete facts, missing evidence, wrong format and unauthorized access separately.
Include the cost of preparing examples, maintaining documents and reviewing outputs. A small gain on an average score can be a poor trade if a critical failure remains. Choose the approach that meets the workflow’s requirements with the clearest maintenance plan.
Common questions
Does RAG stop all hallucinations?
No. Retrieval can select the wrong material, and generation can still make unsupported claims.
Is fine-tuning a live company knowledge base?
No. Parameter updates do not automatically track edits to company records or enforce user access.
Can RAG and fine-tuning be used together?
Yes, when evaluation justifies both. Test what each contributes before adding complexity.
Sources and notes
- Lewis et al. — Retrieval-Augmented Generation
The original RAG paper for knowledge-intensive language tasks.
- Google — Fine-tuning, distillation and prompt engineering
How adaptation methods differ.
- Google — Dividing datasets
Separating training, validation and testing.
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