Model Fine-Tuning: A Short Guide

30.06.2026 Fine-tuning sounds like a way to make the model your own

: take an open LLM, train it on your data, and get an expert in your domain. Sometimes that is true. More often, it is not.

Fine-tuning solves a narrow but real class of tasks: when the model must run inside a closed perimeter on accessible hardware, without the cloud and without corpus search on every request.

Beyond that, cloud RAG or llm-wiki discipline is cheaper and more reliable.

This text is about how to tell one from the other, without extra theory and without made-up economics.

This is an analysis of one way to give an LLM your knowledge from a shared cluster.

A map of all the approaches and how they work together - in the overview "How to Give an LLM Your Knowledge".

A model fine-tuned on a company's corpus of similar contracts reasons like that company's specialist lawyer: it knows their contract structure, standard wording, and what to check first.

It runs within your perimeter - contracts never leave the cloud - and does not require RAG at runtime.

Two conditions without which the example does not work. First: the patterns and style must be stable.

If contract templates and interpretations change every month, the advantage fades, and the question shifts to RAG/llm-wiki over a live corpus. Second: fine-tuning fixes the style and structure of reasoning, but does not guarantee factual or legal correctness - a human still has to check that.

The most common practical answer is both layers.

Fine-tune the model for domain behavior and format, and use RAG for fresh facts.

Then what is stable is built in (style, structure, vocabulary), while what changes lives outside (numbers, documents, rules).

The tree below helps determine which way a specific task leans.

Yes, KT.Team fine-tunes models, including so the model can run locally, on available hardware, and inside your perimeter.

This is appropriate when the task falls into the "right" branch of the tree: privacy, edge/offline, high call volume, a stable domain with specific behavior.

Because these are open models in your own environment, you own the system and the weights - without dependence on a cloud vendor, its pricing, or its availability. And just as honestly: if your knowledge changes often, you have no dataset, and you need links and auditability, you do not need this.

Then we will build a cloud setup with RAG or an llm-wiki discipline, and it will be cheaper to own and easier to operate.

Often the result is a combination: fine-tuned behavior plus RAG for fresh facts.

If data must be kept under Federal Law 152 and anonymized before entering the model, this is the layer LLM Gateway, it works with both a fine-tuned and a cloud model.

We work in short iterations: first a lightweight adapter and an effect measurement, then expansion - so you pay for the result, not the promise.

How we fine-tune models as a service - dataset, LoRA, before/after metrics, and rollout - Model fine-tuning (ML).

A complete map of ways to give an LLM knowledge and how they combine - "How to Give an LLM Your Knowledge".

Understanding versus execution, weights versus context - "Karpathy's Lens". RAG as a runtime retrieval mechanism - RAG tool.

Corporate memory based on the "No RAG" principle - Sloy.

Models for Federal Law 152 and anonymization at input - LLM Gateway.

AI implementation in short iterations - for business.

Model cost figures and a deployment calculator - LLM 2026 breakdown.