RAG vs Fine-tuning: which one to choose?

Client :

I need to customize an LLM. Should I use RAG or fine-tuning?

Me :

The right question is not “which is better”, but “which problem are we solving”.

In many cases, you do not need RAG or fine-tuning right away. A strong prompting strategy is often enough to create immediate value.

Start with prompting first (often enough)

Prompting means clearly defining the model role, context, constraints, and output format.

Examples of instructions that can dramatically improve output:

• “Answer as a compliance advisor, in 5 concise bullet points.”
• “If information is missing, say it clearly instead of guessing.”
• “Use this format: Risk / Impact / Recommendation.”

This is often sufficient when:

• the need is mostly about writing quality or response structure,
• key information can be provided directly in the prompt,
• you want to validate value quickly before investing further.

Prompting is usually the best starting point: fast, low-cost, and easy to iterate.

When to choose RAG

RAG (Retrieval-Augmented Generation) is best when knowledge changes often and needs traceability.

Typical cases:

• evolving product documentation,
• legal/compliance references that must be cited,
• internal knowledge bases updated frequently.

Benefits:

• quick updates without retraining,
• source-grounded answers,
• better freshness control.

When to choose fine-tuning

Fine-tuning is better when you need persistent behavioral changes:

• brand tone and voice,
• standard output structure,
• specialized behavior on recurring tasks.

It is most effective when the target behavior is stable and you have high-quality examples.

What I recommend

A hybrid strategy is usually best:

1. Structured prompting to frame the response.
2. RAG for dynamic business knowledge.
3. Lightweight fine-tuning for output quality and consistency.

In short: prompting frames the mission, RAG controls what to know, and fine-tuning stabilizes how to respond at scale.

Common mistakes

• fine-tuning too early without clean data,
• using only RAG when format quality is critical,
• skipping metrics for precision, latency, cost, and hallucination rate.

An example

Take a legal operations team that needs to answer internal contract questions quickly.

• If source documents change often, RAG should come first because it pulls the latest clauses.
• If every answer must follow a strict structure (risk, recommendation, next action), a light fine-tuning layer can help.

In many cases, the combination works best: up-to-date knowledge plus consistent response style.

I recommend asking 4 questions

To make a solid decision quickly:

1. Have you seriously tested a well-structured prompt first?
   If not, start there.
2. Does knowledge change every week?
   If yes, add RAG.
3. Is output format business-critical?
   If yes, consider fine-tuning.
4. Do you have enough high-quality examples?
   If no, do not fine-tune yet.

This keeps decisions tied to business constraints, not technology trends.