# Fine-tuning vs RAG: the Slash AI Lab criterion

Source: https://slash-digital.io/en/insights/fine-tuning-vs-rag/

Agents and engineering · Updated September 2026 By default, RAG: when knowledge must stay current and citations matter. Fine-tuning when style, language or a narrow skill doesn't fit in context. Hybrid when both are true.

- Default RAG
- When it fits LoRA adapters
- Rule constraints before recipes

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All insights

Jean Martial, CEO and co-founder, Slash Digital Lab

Published: September 23, 2026

Updated: September 26, 2026

Reading time: 3 min

Topic: Agents and engineering

In short
Fine-tuning and RAG don't compete: they solve different problems. This is the decision order we follow before spending a single peso on training.

For your company
RAG, fine-tuning or both? Slash Digital Lab adapts models to your domain, language and style, and measures whether they really improve.

Fine-tuning & RAG →

## RAG first, almost always

Changing knowledge and required citations: RAG.

If your knowledge changes (catalogues, policies, documentation) or your answers must carry citations, RAG is the default: index, re-rank and cite, without touching the model.

RAG fails when the problem isn't knowledge but form: tone, output format, or a narrow task where the generic model falls short of your quality bar. That's where fine-tuning comes in. If you want to decide which of the two fits your case, Slash supports companies with LLM fine-tuning and RAG.

## Fine-tune for form, not facts

LoRA adapters for tone and format; facts stay in RAG.

Fine-tuning wins when you need the model to sound, format or execute like your organization: editorial style, fixed output structures, a narrow domain with stable rules.

At Slash we use LoRA adapters for that layer: we don't retrain the world. Cost stays bounded, rollback is possible, and knowledge keeps living in RAG, where it can be updated.

## Hybrid, when both are true

Adapters for form, RAG for data.

When the style must be yours and the knowledge must stay current, hybrid wins: adapters for form, RAG for data. It's the most common configuration in Slash deployments and the cheapest to maintain.

## Four questions before paying for training

Answer them in writing; the route follows.

Before paying for training, answer in writing: is the problem knowledge or form? Does your data change daily or stay stable? Must citations be verifiable? Must the knowledge stay in-house? The answers define the route; everything else is consultant margin.

### Key takeaways

- RAG by default: changing knowledge and required citations.
- Fine-tune only for form: style, format and narrow tasks.
- Hybrid when both are true.
- Nothing is decided without a measured baseline.
- Local inference when compliance demands it.

## Sources

- Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks · Lewis et al., arXiv, 2020-05-22
- LoRA: Low-Rank Adaptation of Large Language Models · Hu et al., arXiv, 2021-06-17
- QLoRA: Efficient Finetuning of Quantized LLMs · Dettmers et al., arXiv, 2023-05-23
- Introducing Contextual Retrieval · Anthropic, 2024-09-19

Editorial note: this analysis reflects the public information available on the review date. Models, prices and rules change fast; every third-party figure links to its source, and our opinions are labeled as such. Spotted an error? Write to contact@slash-digital.io.

Frequently asked questions

## The questions we hear often

**When is fine-tuning NOT worth it?**
When the problem is missing or changing knowledge: RAG handles that with less cost and less risk.

**What's a LoRA adapter?**
A small layer trained on top of the base model for form and narrow tasks: cheap, reversible and compatible with local inference.

**Can RAG and fine-tuning be combined?**
Yes, that's the usual production pattern: adapters for form, RAG for cited knowledge.

**How much does an experiment cost?**
It's scoped in sprints and the diagnosis sets the size. Write to us with your case.

**How is the outcome measured?**
Against a baseline of the best generic model, with comparable quality, cost and latency metrics.