Fine-tuning custom models
We fine-tune language models for your style, tone and task type — e.g. so the model answers in your brand tone or a specific format. Honestly upfront: fine-tuning is NOT a way to 'inject knowledge into the model' (for facts RAG — 893 — is more reliable and cheaper); it adapts STYLE/BEHAVIOR, requires quality labeled data, is expensive, and must be redone when the base model changes. Often good prompts (890) or RAG solve the task more cheaply — we will honestly say whether you need fine-tuning at all.
Fine-tuning custom models — overview
Fine-tuning is additional training of a ready language model on your examples so it stably behaves in the needed style/format/tone or handles a specific task type better. Honestly about the main misconception, this is key: fine-tuning is NOT a way to 'load your knowledge/documents into the model'. For answers from facts and current data RAG (893) is more reliable, cheaper and simpler: it relies on your documents, gives links and updates instantly. Fine-tuning changes the model's STYLE AND BEHAVIOR, while as a way to 'inject facts' it is unreliable (the model may still confuse things) and expensive to update. This is a frequent and costly misconception, and we honestly dispel it. Honestly about requirements: fine-tuning requires a sufficient volume of QUALITY labeled examples (bad data → bad model), compute resources and expertise. It is more expensive and slower than prompts and RAG. Honestly about 'not needed by everyone': in most tasks good prompt engineering (890) and/or RAG (893) give the needed result more cheaply and faster. Fine-tuning is justified when: a stable specific style/format hard to hold with prompts is needed; a narrow repetitive task with good data; latency/cost requirements at scale. We will honestly assess whether it is your case rather than take on an expensive project. Honestly about maintenance: a fine-tuned model is tied to the base version; when it updates/becomes obsolete, fine-tuning must be repeated. Honestly about the effect: when applied correctly it gives stable needed behavior but does not 'make the model smarter at everything' and does not guarantee a business result. Honestly about access: quality training data is needed. An important boundary: this is fine-tuning; for knowledge/facts — RAG (893); for behavior more cheaply — prompts (890). Picture this: instead of 'expensively fine-tuning for what a prompt solves' — an honest assessment and fine-tuning only where it is really needed. The base price starts from 80,000 ₽ (depends on data and the task).
Problems we solve
- A stable specific style/format of model answers is needed.
- Prompts cannot stably hold the needed behavior.
- There is a temptation to 'fine-tune AI on our data' for knowledge.
- It is unclear what to choose: prompts, RAG or fine-tuning.
What's included in the Fine-tuning custom models service
- An honest assessment: do you need fine-tuning or are prompts/RAG enough
- Preparing and checking the quality of training data
- Fine-tuning the model for style/behavior/format
- Result evaluation and comparison with prompts/RAG
- Honest boundaries (not for knowledge; for facts — RAG; expensive; repeat on model change)
- A maintenance plan (tie to the base version)
- A link with prompts (890) and RAG (893)
- Handover and review with you
What you get
- A model with stable needed style/behavior (where justified)
- An honest choice of approach (often cheaper — prompts/RAG)
- Understanding the cost and maintenance of fine-tuning
- Realistic expectations (not 'smarter at everything'; knowledge via RAG)
How the work goes: steps
- We assess the task: fine-tuning vs prompts/RAG; collect data
- If justified — prepare data, fine-tune, evaluate the result
- We build in maintenance, honestly set boundaries with you
Why PDV Expert
- Fixed price and timeline — no surprises on the invoice.
- Report and recommendations in plain language — clear without a technical background.
- In touch at every step and answering questions about the result.
FAQ
Fine-tune the model on our documents so it knows them?
This is a frequent and costly misconception, honestly. Fine-tuning is not a reliable way to 'inject knowledge': for answers from your facts/documents RAG (893) is better — it relies on data, gives links and updates instantly. Fine-tuning changes style/behavior, while as a fact store it is unreliable and expensive to update. For knowledge we almost always recommend RAG, not fine-tuning.
Does my project definitely need fine-tuning?
More often — no, and we will honestly assess. In most tasks good prompts (890) and/or RAG (893) give the needed result more cheaply and faster. Fine-tuning is justified for a stable specific style/format, narrow repetitive tasks with good data, or cost/latency requirements at scale. We will not take on expensive fine-tuning if a prompt solves the task.
Fine-tuned once — and use it forever?
No, honestly: a fine-tuned model is tied to a specific base version. When the base model updates or becomes obsolete, fine-tuning must be repeated on the new version. This is part of the cost of ownership. Plus quality data is needed upfront. We honestly factor this in rather than promise 'trained forever'.
About the provider
The «Fine-tuning custom models» service is provided by PDV Expert — a team specialising in «Site quality». We work under contract and deliver a written report with recommendations.