Fine-tuning vs RAG: when does your company really need a custom model?

Many companies ask whether they need to train a custom AI model. In most internal knowledge use cases, the better first question is different: do you need the model to learn a behavior, or do you need it to answer from current company documents?

Those are different problems.

What RAG is good for

RAG retrieves information from external sources before the model answers. It is useful when the answer depends on documents that change:

• Policies.
• Contracts.
• Product documentation.
• Process manuals.
• Client notes.
• Support articles.
• Internal playbooks.

RAG also allows the assistant to cite sources, which is essential for trust.

What fine-tuning is good for

Fine-tuning is useful when you want a model to behave differently in a repeated pattern:

• Follow a specific output format.
• Classify messages in a consistent way.
• Use a specific tone.
• Perform a narrow task from many examples.

It is usually not the best way to store dynamic company knowledge.

The common mistake

The mistake is using fine-tuning to solve a retrieval problem. If the company wants answers from current documents, training a model once will not keep the information up to date. It also makes source citation harder.

A practical decision rule

Use RAG first when:

• Knowledge changes.
• Sources matter.
• Permissions matter.
• Documents are the source of truth.

Consider fine-tuning when:

• The task is stable.
• You have enough high-quality examples.
• The desired behavior is repeated.
• The answer does not depend on constantly changing documents.

The practical conclusion

Most SMEs and operational teams do not need a custom model first. They need their knowledge connected, searchable, permission-aware, and cited.

Polp starts there: RAG over company documents and tools, with controls for real business use.

Sources:

• Retrieval-Augmented Generation Survey
• OpenAI Cookbook
• Microsoft Learn
• Andreessen Horowitz