Fine-Tuning of GigaChat: Where the Base Model Breaks on Data
Fine-tuning of GigaChat is needed where the base model answers general questions confidently and falls apart on a company's internal data.
For it, product nomenclature, department abbreviations, names of tariffs and regulations are a set of symbols without stable connections: it has seen similar words in open texts, but has not seen how they relate to each other specifically in your environment.
This leads to typical failures: the model confuses similarly spelled items, substitutes a general rule for a specific one, and answers smoothly but off target. Adapting a language model to a domain does not come down to a single instruction in a prompt.
A prompt sets the tone and response format, but you cannot embed knowledge that is not there: the model will retell a reference book in context, but it will no longer connect an SKU to a category, a return reason to a defect type, or a regulation clause to a specific step.
Lengthening the instruction does not solve the problem: the more reference data in the prompt, the more attention to detail is dispersed, and the entire reference book has to be passed on every request.
This is exactly where answer quality breaks down in a chat assistant: a specialist double-checks every second conclusion, and instead of speeding up work, the tool adds another step.
At the same time, base model errors on internal data look plausible — a confident wording without factual support.
Fine-tuning transfers knowledge into the model itself: it learns from your "question — correct answer" pairs, on nomenclature, support correspondence, and internal regulations, not on their description.
We explain how this works and where the boundary lies between what is covered by fine-tuning and what is more honestly solved by document search.
What Adapting a Model to a Subject Domain Delivers
Fine-tuning changes not the style of answers but their distribution.
On a pilot sample of real requests, we compare two versions of the model on the same questions: the base GigaChat noticeably "drifts" in a narrow subject domain, while the fine-tuned one maintains industry-accepted terminology and answer structure.
The key adaptation metric is the share of correct answers to industry questions and its practical consequence: the share of answers accepted by an operator without edits.
We calculate it not as an average score across the entire set, but as a breakdown by category — regulatory question, calculation, reference request, situation analysis: averaging hides failures in individual categories, and those are exactly what determine whether the tool enters the workflow.
Reproducibility of wording is more important than a single successful answer.
On semantically similar requests, the adapted model maintains one presentation pattern and does not change interpretation from run to run — the variance of answers on the control sample drops, the operator stops double-checking every second line, and manual edits narrow to cases where fresh input is needed.
A whole class of failures also disappears: invented regulation clauses, nonexistent details, links to documents that do not exist.
Public studies on language model adaptation record the same pattern — a measurable gain in a narrow domain comes from a moderate volume of carefully labeled "question — answer" pairs, whereas a raw document dump tends to blur the result.
We measure generative model quality metrics on a held-out set that the model did not see during training: otherwise the accuracy gain would be a consequence of memorizing examples rather than understanding the subject domain.
Model Adaptation Methods: Comparison of Approaches
GigaChat adaptation methods solve different tasks, and substituting one for another is a typical mistake. Prompts and instructions change model behavior without modifying weights: the result is visible immediately, and rollback is one line of configuration.
Search over an internal knowledge base connects facts that are not in the model and updates together with documents. Fine-tuning on examples rebuilds style, response format, and industry logic where instructions can no longer hold them.
| Option | When It Fits | Limitations |
|---|---|---|
| Prompts and instructions | Rules, tone, and answer structure change often, and a quick start is needed | A long instruction occupies the context window, behavior remains probabilistic |
| Search over a knowledge base | Answers rely on regulations, contracts, catalog, request history | Prepared sources and their labeling are needed, and an extraction step is added |
| Fine-tuning on examples | A stable format, proprietary terminology, and domain logic are required | A corpus of "question — exemplary answer" pairs is needed, and updating is more expensive |
| Combination of approaches | Knowledge lives in the database, while style and format live in the model | Longer preparation, a single source of truth and version control are needed |
The choice of adaptation method comes down to three questions: where the knowledge lives, how often it changes, and how strictly the answer format is defined.
Data updated daily is handled by database search, while an identically sounding answer across thousands of requests is handled by fine-tuning. Prompt and instruction configuration remains a mandatory layer in any option: it sets the frame within which the others operate.
GigaChat Fine-Tuning: Stages of Engineering Work
GigaChat fine-tuning is a sequence with checkpoints, not a single training run. We fix acceptance criteria before the first data edit: if a metric cannot be calculated on a held-out set, the stage is not closed.
- Task and acceptance criteria analysis — we determine what the model should do differently from the base one: answer format, domain terms, correct refusals. We immediately choose metrics and a threshold below which we do not accept the result.
- Data audit — we review request logs, regulations, and the knowledge base. We assess volume, duplicates, personal data, and terminology discrepancies; we filter out the excess and bring the rest to a unified form.
- Training set labeling — we prepare labeling rules and reference "request — answer" pairs, label a pilot block, and check consistency between annotators. If it diverges, we fix the instruction, not the data.
- Training — we select the mode and parameters based on the validation part and ensure that the model does not lose general skills for the sake of domain ones. We save checkpoints: a rollback path is needed.
- Held-out validation — we calculate the metrics from the first step on examples that did not participate in training and compare with the base model.
- Acceptance by quality metrics — we compile results into a report: where there is improvement, where there is parity, and what goes into production.
- Handover and team training — we provide configurations, labeling instructions, and the procedure for fine-tuning on new data.
The model implementation stages follow one order — from acceptance criteria to team handover. Updating on new data goes through the same cycle, and quality is compared with the already accepted version.
Handover Artifacts: Weights, Diagrams, Regulations
Handover is not "we sent the weights and said goodbye." GigaChat fine-tuning ends with your team having a kit sufficient for independently launching, updating, and rolling back the model — without contacting us for every step.
- Weights of the fine-tuned model and adapter files — an artifact that is deployed in your environment reproducibly, according to a fixed configuration.
- Model card and applicable boundaries: base model, training sample size, metrics on the held-out set, and honestly described boundaries — which topics and answer formats the model handles confidently, and where it starts to make mistakes.
- Environment diagram: where weights are stored, where inference is performed, and how request preprocessing, logging, and answer quality evaluation are structured.
- Data access policies: roles and permissions, what is masked before being written to the log, retention period, and rules for exporting a sample for retraining.
- Startup scripts and environment configurations — deployment and health checks on a clean machine are repeated with one command.
- Update and rollback regulation: how to accumulate new examples, by which metrics to accept a version, who signs off on the release, and how to return to the previous adapter if quality drops.
- Acceptance run protocol on your request set: what it was before fine-tuning and what it became after.
All this goes into your repository and documentation, rather than remaining in our correspondence. If you already have your own practice for some item — for example, logging is done in a shared environment — we adapt the kit to it, not the other way around.
Case Study: Support Request Triage After Adaptation
The support department triaged requests in a shared queue: the operator determined the topic themselves, looked for a standard answer, and rewrote it for the client.
With several thousand requests per month, this resulted in a long first response and a noticeable share of emails sent to the wrong specialist — they were forwarded again, and the client waited twice.
What It Was Before
Enough historical requests had accumulated, but there was no unified labeling: topics overlapped, some were duplicated in wording, and operator answers diverged in details. Training a model directly on such a slice means getting confident errors on rare but costly topics.
What We Changed
We assembled a corpus of closed requests, reduced topics to a fixed list, and selectively rechecked the labeling: disputed cases were resolved by a written rule, not by an individual operator's decision.
On this corpus, fine-tuning of the model based on GigaChat was performed — it determines the topic and suggests a draft answer, which the operator edits before sending.
What It Became
The final word remained with the human: on nonstandard topics, the model marks the answer as requiring review and does not send it itself. Metrics before and after implementation were calculated on the same list of categories — otherwise the comparison diverges due to relabeling.
| Metric | Before | After |
|---|---|---|
| Request routing accuracy | 71% | 93% |
| Requests without forwarding | 78% | 96% |
| First response time | baseline | approximately half as long |
The main takeaway from the model fine-tuning case: the result comes not from model size, but from corpus quality and rules written in advance for disputed cases. If labeling is not consolidated before training, the model simply reinforces the old queue discrepancies.
What to Do If the Model Is Already Deployed in the Company's Environment
If the assistant is already working in the environment, the task is not to replace it, but to swap the model underneath it — without stopping the service.
Therefore, we deploy the configured version alongside the current one and switch traffic gradually: first to internal checks, then to one team, then to the entire environment.
The previous version remains operational until the last step, and rollback takes one operation, not restoration from backups.
Current instructions are transferred almost entirely: role and tone, regulations and prohibitions, terminology dictionaries, templates for standard answers, and rules for accessing reference books.
The rework boundary lies where the old rules were workarounds — duplicate constraints, manual format adjustment, and verbose prompts.
The configured model maintains style and structure itself, so long instructions begin to get in the way: we remove them, and the freed-up space goes to reference examples.
The assistant migration plan is a sequence of steps: inventorying live scenarios, fixing a reference sample of requests and correct answers, assembling the configured version on this data, running a shadow test on the real stream with comparison by match rate with the reference, refusal shares, and response time, then gradually switching traffic and consolidating the result — updated instructions, documented rollback, and a support regulation for your team.
GigaChat fine-tuning in such a scenario is a controlled core replacement: the service continues to respond, and quality grows on the same scenarios.
Technical Concerns: Data, Volumes, Risks, Timelines
Before starting GigaChat fine-tuning, customers ask the same questions — they concern not the model but process manageability: the volume of examples, labeling quality, result validation, the risk of losing general knowledge, and working in a closed environment.
How much data is needed for training?
The threshold is set by the task: for field extraction and request classification, several hundred consistently labeled "request — reference answer" pairs are enough; for answer style and tonality, one to two thousand.
A thousand examples under one rule work better than three thousand contradictory ones, so we assemble the starting set from accumulated correspondence and separately add rare scenarios.
What to do if labeling is weak?
If examples were labeled by different people without a regulation, the model will learn noise and contradictions.
Before training, we run the set through reconciliation: we find conflicting pairs, send disputed cases to your expert for a decision, and fill the remainder with synthetic examples under human supervision.
How is model quality checked?
On a held-out set that the model has not seen: we look at the share of correct answers, recall on rare classes, and robustness to paraphrasing. Acceptance is based on your task's metrics and comparison with baseline behavior, not on a general impression from individual answers.
Will the model lose general knowledge?
There is a risk: training on a narrow set displaces previous skills. Therefore, we mix target examples with general ones, limit the impact on weights, and before release run a checklist of out-of-topic questions — answers to them should not degrade.
Is it possible to work in a closed environment?
Yes: training and inference are deployed in your infrastructure, data does not leave the perimeter, access is granted by role, and datasets and resulting weights are stored with an operations log. If necessary, we work on an isolated test bench without external network access.
Let's Discuss the Task: Where the Project Begins
To discuss a fine-tuning task substantively, a ready technical document is not needed — a description of the process is enough. Tell us which requests the model should handle, where it currently makes mistakes, and what is considered a correct answer in disputed cases.
- Where the boundary lies: which requests the model closes itself, and which it must pass to an operator.
- What data already exists — support correspondence, knowledge base, regulations, exports — and in what form it is.
- What counts as an error: an inaccurate answer, an invented document link, refusal to answer, a change in tone.
- Response constraints: how many seconds it is acceptable to wait for an answer and what volume of requests must be sustained.
- Who on your side accepts the result and by which examples quality will be checked.
- Where the solution is integrated — website, support chat, internal system — and how it will get access to data.
In response, we send a solution diagram, scope of work, and a stage plan with control points: where we prepare and label data, where we train, and where we validate on a held-out set.
The estimate for a model adaptation project relies on the volume and quality of data, not on general assumptions, so it does not fall apart after the first clarification.
Describe the task in free form and attach several dialogue examples — we will return with an estimate and plan. This is enough to schedule the project start and fix the order of work.
