RAG or Fine-Tuning? How AI uses your company knowledge

The most important points at a glance

RAG: Answers from current documents, with source and permissions.
Fine-tuning: changes the model, suitable for style and format.
For company knowledge RAG is almost always the better choice.
Fine-tuning with personal data is problematic from a data protection perspective.

If you want to use AI with company knowledge there are two ways: RAG provides the language model with the relevant passages from your documents for each question, Fine-tuning retrains the model itself. For facts that change – prices, processes, policies, contracts – RAG is almost always the better choice: up to date, verifiable, more cost-effective. Fine-tuning is more suitable for style, format, and highly specialized terminology.

The difference in one image

Imagine a new employee. RAG means: for every question, she is given the relevant folder on her desk and answers from it – with a reference to the page. Fine-Tuning means: she is trained for weeks until she can memorize a lot – but she does not know what has changed since the training, and she cannot substantiate where a statement comes from.

RAG in detail

Current: New or modified documents are ingested and available immediately.
Traceable: Answers can cite the source.
Permissions:Answers can be restricted to documents that the person asking is allowed to see.
Cost-effective: No training, the model remains unchanged.
Limitation: The answer is only as good as the search – poorly prepared documents lead to weak answers.

How to build such a system is shown by Build a RAG system in the company.

Fine-tuning in detail

Style and format: The model learns to respond in a specific tone or fixed structures.
Technical language:For very specific terms, it can improve understanding.
Effort:It requires training data of good quality, computing power, and expertise.
Limitation:Factual knowledge is frozen at the state of the training; sources cannot be cited; errors are difficult to correct.
Data protection:Personal data in training data can hardly be removed from a model once it has been trained.

Comparison by criteria

Recency: RAG immediately · Fine-tuning only after retraining
Source attribution: RAG yes · Fine-tuning no
Access rights: RAG feasible · Fine-tuning practically not
Effort: RAG prepare data · Fine-tuning training data + training
Well suited for: RAG facts and knowledge · Fine-tuning style, format, technical language

And the combination?

In individual cases, both are combined: a lightly fine-tuned model with a specific response style also uses RAG for the facts. For most SMEs, this is unnecessary – modern language models can already be controlled well through clear instructions ("Respond briefly, objectively, using formal address").

Typical misconceptions

"We need to train the AI with our data." – In most cases, RAG is sufficient; in practice, "training" often means curating content. Example: Train AI chatbot for the DACH region.
“Fine-tuning is safer.” – On the contrary: What is embedded in the model can hardly be controlled or deleted.
“RAG cannot handle context.” – Good systems combine multiple sources into one answer.

For those interested in technology

When fine-tuning open models, LoRA is often used – it changes only a small part of the weights and saves computing effort.
RAG quality depends heavily on Chunking, Embedding model and reranking – see Embeddings simply explained.
Both methods can be implemented with open models on servers in Germany.

bettersorted relies on open, transparent components for its customers – operated on servers in Germany and bundled in the automaisa Hub. We provide vendor-neutral consulting, are a BAFA-registered consultant and an authorized INQA coach. The Consulting and guided implementation can be subsidized through INQA-Coaching at 80%; the Funding Check shows the right path. For an initial, non-binding consultation: Contact.

Example: When a law firm considered fine-tuning — and opted for RAG instead

An illustrative scenario: A law firm wanted to “train a model on all pleadings” so it could answer questions about past cases. The review showed: pleadings change continuously, contain client data, and answers must be verifiable. With RAG, the system now answers questions from the current case files with source references and only for authorized persons. The desired matter-of-fact style is achieved through a clear instruction to the model — without any training at all.

Decision tree in four questions

Are you dealing with facts that change? → RAG.
Do answers need to be verifiable? → RAG.
Do different access rights apply? → RAG.
Is it only about style or format that cannot be achieved through prompting? → possibly fine-tuning.

Costs and funding

The costs of using company knowledge with AI consist of three parts: Setup (planning, installation, integration, testing), ongoing operation (server or hosting, updates, monitoring, data backup) and support for employees (training, rules, points of contact). We do not quote fixed prices because the scope and starting point vary greatly. The software itself does not incur license costs for open-source solutions.

Funding is not for the technology, but for the Consulting and guided implementation: Through INQA-Coaching the federal government covers 80% of coaching costs nationwide (up to €11,520, vouchers until 30.06.2028). A preliminary analysis can be subsidized through the BAFA consulting grant – with 80% in the new federal states, Lüneburg and Trier, otherwise 50%, for applications until 31.12.2026. For investments, some states offer their own programs – see Funding opportunities for companies in Germany.

Frequently asked questions

What is the difference between RAG and Fine-Tuning?

RAG provides the language model with relevant passages from your documents for each question; the model itself remains unchanged. Fine-Tuning retrains the model itself and permanently changes its behavior.

What is better suited for company knowledge?

In the vast majority of cases, RAG: content stays up to date, answers can be verified, and access rights can be implemented.

When is fine-tuning useful?

When a model is supposed to learn a specific style, a fixed format, or very specialized domain language — not for facts that change.

Is fine-tuning problematic from a data protection perspective?

It can be problematic because personal data from training data ends up in the model and is hardly removable. With RAG, data remains in the knowledge base and can be deleted.

As of October 2026.