RAG or fine-tuning: choosing before training the team
RAG vs fine-tuning: distinguish knowledge, behaviour, updating and evidence before training your team on an AI project.
By Pablo Nastar
Comparing RAG and fine-tuning only makes sense after naming the problem. If the assistant must consult documents that change, supplying a retrieved context may be relevant. If it must adopt a stable format or behaviour, adapting the model can be studied. These techniques do not make a source true and do not replace data governance.
The simple rule to remember: use documents to bring knowledge, and consider adapting the model only for an evaluated target. Many projects first need a better corpus and a test protocol.
Contents
We distinguish RAG and fine-tuning, build a baseline, identify the necessary skills and data, then examine the combinations and the limits.
RAG brings context to the query
Retrieval-augmented generation searches for passages in a base and then supplies them to the model to produce the answer. It can make updates and citations easier, provided the search finds the right documents.
The documentation of Google Cloud on RAG describes this general principle. The implementation may include chunking, indexing, search and generation. Poor segmentation or a missing filter can discard the needed passage.
The test must therefore separate two errors: the passage was not retrieved, or the model misinterpreted a correct passage. Without this distinction, the team fixes the wrong layer.
Fine-tuning adapts a behaviour on examples
Fine-tuning uses examples to steer the behaviour of a model on a task. It is not a documentary base that is simple to keep up to date. It requires a quality dataset and an evaluation before and after adaptation.
The OpenAI documentation on fine-tuning presents the steps and uses of its offers. Other providers have their own methods. Check the specific platform, the admissible data and the retention terms.
| Need | First avenue | Why |
|---|---|---|
| answer from up-to-date procedures | RAG | corpus that can be modified and cited |
| respect a stable output format | prompt then possibly fine-tuning | measurable behaviour |
| fix a contradictory base | neither one | documentary governance first |
| guarantee an exact decision | neither one alone | business control required |
Start with a baseline without adaptation
You cannot measure the improvement without a reference. First test a clear prompt with a limited corpus. Note the errors, the correction time and the cases where the tool must refuse.
Step 1: define the task. One output, one user, one risk level.
Step 2: create a test set. Include normal cases, contradictions and missing information.
Step 3: test the base model. Keep the answers and the configuration.
Step 4: try RAG. Measure retrieval and generation separately.
Step 5: study fine-tuning if a behaviour defect persists. Do not use it to mask a weak corpus.
Hypothetical implementation. A company could index public demonstration procedures and ask for answers with citations. If the passages are retrieved but the format stays unstable, it would then test an adaptation on synthetic examples. This scenario does not describe an Educasium client project.
The skills are not the same
A RAG project requires documentary and search skills; fine-tuning requires a discipline of example data and evaluation. Both demand security, supervision and business understanding.
The training must cover the full path: source, preparation, test, error and review. The guide to AI training in the company helps distribute the roles. The guide on the confidentiality of AI tools helps identify the controls to apply to the corpus and the outputs.
A team that does not know how to maintain its documents should not start with a more complex architecture. The technical project amplifies existing practices.
Data, evidence and responsibility
Neither RAG nor fine-tuning automatically turns content into a reliable source. The rights, the version, the provenance and the retention rules still have to be managed.
For an internal corpus, control the access and the metadata. For fine-tuning examples, remove the useless data and check the authorisations. The CNIL provides resources on AI and personal data. The PDF introductory guide to artificial intelligence lays an educational base before working on the data.
Bounded experience
Educasium trains in the concepts and the project decision; it does not publish a proprietary benchmark or a RAG or fine-tuning deployment at a client here. Our view is that most teams must first learn to build a baseline and a test set.
This discipline prevents a costly architecture from being judged on a few favourable demonstrations. It also allows stopping if the documentary process is not ready.
The decision is then based on observed errors, and not on the novelty of the technique or an isolated demonstration.
Limits and counter-cases
A RAG can retrieve a passage out of context, miss an exception or expose a document to an unauthorised user. A fine-tuning can learn errors or become hard to explain. Both add maintenance.
For a small, stable collection, a classic search and response templates may suffice. For a critical decision, a competent person validates the source and the conclusion. Technical sophistication does not lower that responsibility.
Frequently asked questions
Does RAG train the model on my documents?
The principle of RAG is to supply passages at query time, not necessarily to retrain the model on the corpus. The implementation and the services used must nonetheless be checked. Documents may be stored and processed across several layers. Map the real flow.
Does fine-tuning add up-to-date knowledge?
That is not its simplest or most maintainable use. It mainly serves to adapt a behaviour from examples depending on the platform. Information that changes is often better handled in a versioned corpus. First test a retrieved context and clear instructions.
Can you combine the two?
Yes, if each layer solves a demonstrated problem. A system can retrieve sources and use a model adapted to a format. That combination also increases complexity and maintenance. You must measure the gain against a simpler baseline.
Who should be trained?
The corpus owners, the business users, the technical people and the data owners. Not everyone needs the same level. All must understand provenance, limits and the escalation path. A single expert is not enough to operate the system durably.
Decide before architecting
Training eligible for OPCO/FIF PL funding subject to conditions. Qualiopi-certified organisation. Discover the introduction to AI and prompting training or contact Educasium to build a decision grid adapted to your corpus.