When the base model errs: why fine-tuning Gemini is needed
A general-purpose model confidently answers general questions and errs where your subject domain begins.
It substitutes a similar-sounding but incorrect term from an internal classification, confuses a regulation with an instruction, and produces a smooth paragraph in which not a single wording can be used without verification.
Base model errors are dangerous not by frequency but by the form of delivery: they look like a finished answer.
Alongside lives a second pain — unstable response format. Today the model returns the required structure with a full set of fields, tomorrow it changes the order, adds explanations of its own, or merges two fields into one, and automatic processing of the result stops.
Responses have to be parsed and corrected by hand, and the share of such cases does not decrease over time.
Instructions and examples in the prompt narrow the variance but do not change the model's understanding of your terminology and do not hold the structure rigidly. Adapting the model to a subject domain requires a different tool, and here fine-tuning Gemini begins.
Fine-tuning a language model on your "question — reference answer" pairs transfers into the model both the subject-domain vocabulary and the formatting rules: plausible but incorrect wordings go away, the format stops drifting, and manual review shrinks to spot checks.
Next we break down how this is covered: what data is needed, how the result is measured, and when fine-tuning yields to simple instruction configuration.
What changes after fine-tuning: accuracy, format, query volume
Fine-tuning changes not the model's "general intelligence" but its behavior on a narrow class of tasks.
We measure the effect on a holdout set of real requests: the share of correct answers on domain-specific wordings grows from 60–70% to 90% and above, while typical failures — term substitution, unnecessary caveats, invented clauses — drop to isolated cases.
The second effect is a unified response format. In Gemini, fine-tuning locks in the output structure: the order of sections, the set of fields, terminology, and tone.
The output is a result suitable for automatic processing, so the share of manual edits after a response falls from most cases to a few per hundred.
The third effect is a reduction in prompt length. Instructions, examples, and rules stop traveling in every request because they are fixed in the model's weights.
The working prompt shrinks severalfold, and along with it the number of auxiliary calls also drops — repeated format checks and clarifying passes.
In sum, this changes the load profile: fewer requests per task, a shorter prompt, higher response predictability.
A failure stops looking like "the model made a mistake" — it is a specific class of wordings that is visible in metrics and closed by the next fine-tuning iteration.
Ways to adapt a model to a task: comparison of approaches
The adaptation method is chosen not by fashion but by data and response requirements: how many labeled examples, how often knowledge changes, how rigidly the format is set.
Fine-tuning Gemini, detailed instructions in the prompt, knowledge base retrieval, and manual rules solve different tasks.
The boundary runs along three axes: volume of examples, speed of knowledge change, and cost of error. If a regulation is updated every week, fine-tuning does not save you — the knowledge lives in the database, not in the model's weights. If instead the style and structure of the response change, fine-tuning is exactly what works.
| Approach | When it fits | Limitations |
|---|---|---|
| Detailed instructions in the prompt | Format and rules are stable, few examples, quick hypothesis validation needed | Consume context window, behavior blurs on long prompts, scale poorly |
| Knowledge base retrieval | Answers rely on updatable documents, source link is important | Quality depends on chunking and retrieval, incorrect assembly of an answer from fragments is possible |
| Model fine-tuning (fine-tuning) | Need a stable style, format, and industry vocabulary, hundreds of labeled examples exist | Does not add new knowledge, requires retraining when data changes |
| Manual rules | Strict checks: field masks, formats, prohibited wordings | Do not work with language variability, grow in volume and conflict with each other |
In practice, approaches are combined: retrieval provides facts, instructions — conversation rules, fine-tuning — a stable style and format, and manual rules cover strict checks. We start with the option that is simple to maintain and add complexity only where metrics require it.
Stages of fine-tuning Gemini: from data audit to handover
Fine-tuning is not a single run but a sequence where each step relies on artifacts from the previous one. An error in preparing examples or choosing quality metrics becomes clear only after training, when redoing it is most expensive. That is why we follow a fixed sequence with approval points.
- Task and data audit. We identify which model errors are critical for the business and check whether a fine-tuning run is needed at all or the task is covered by instructions and knowledge base retrieval.
- Fine-tuning scheme. We determine the adaptation mode for the volume and nature of the data, the format of "query — reference answer" records, acceptance metrics, and the rules for splitting the corpus.
- Example set preparation. We clean duplicates and contradictions, align labeling, and remove personal data. We form the holdout set in advance and do not show it to the model at any training step.
- Training with overfitting control. As soon as quality on the validation part stops growing, the run is stopped rather than pushed to perfection on training examples — otherwise the model loses flexibility.
- Holdout validation. We calculate metrics by error class and compare with the base model. We look not only at the share of correct answers but also at the stability of wordings, adherence to style, and absence of invented facts.
- Handover. We deliver the tuned model, its connection scheme, the test set, and a report with metrics so that your engineers can reproduce the validation on their own.
- Model update regulations. We describe how to replenish the example set from real requests, when to restart training, and how to roll back to the previous version if quality drops.
The regulations are not an appendix for the sake of a document: they turn model quality into a manageable parameter. After handover, you understand what data the model is updated on, who launches it, and which version it corresponds to at a specific moment.
Handover package: datasets, settings, regulations
The handover package is the artifacts that remain with you after the work is accepted, not in our working storage.
We check it at acceptance together with your team: if an outside engineer cannot use it to repeat the fine-tuning and evaluate the result, we consider the package incomplete. Below is the composition that covers both the first run and subsequent operation of the model.
- Labeled datasets — training and validation samples split by periods, labeling instructions, and rules for resolving disputed examples; the share of cases that passed a second review is recorded.
- Fine-tuning scheme — which layers were trained, why such hyperparameters were chosen, how "query — answer" pairs were prepared, and how examples for the required domain were selected.
- Run settings — job configurations, environment requirements, and pinned checkpoints: a repeated run produces a comparable metric, not "roughly the same."
- Data access policies — who and on what basis can see the sample, role separation, masking of sensitive fields, and a log of accesses to the datasets.
- Model documentation — purpose, scope of applicability, known weaknesses, holdout metrics, and a list of typical failures with examples.
- Retraining regulations — triggers (quality drop, new domain, change in internal rules), frequency, responsible parties, and acceptance criteria for a new version.
- Rollback plan — where the previous version and its adapters are stored and how to return to it in minutes if a fresh build degrades on live traffic.
Separately, we hand over a set of control queries for regression: it allows new versions to be compared with each other without re-labeling.
Case study: share of incorrect answers decreased from 38% to 6%
What it was
This concerns a service that answers customer requests using an internal knowledge base.
The base Gemini produced coherent text, but in 38% of cases it diverged from the regulation: deadlines were named at random, the request category was determined incorrectly, and instead of accounting system fields, a free-form paragraph arrived.
A check was applied to every answer — an operator manually verified deadlines and category against the regulation. Errors still got through: some disputed wordings read as correct, and detected discrepancies were returned for rewriting.
What was changed and what was achieved
We assembled a training sample from accumulated dialogues and labeled it against reference answers: separately correct ones, separately those rejected by operators. On this data, we performed fine-tuning of the model.
At the same time, we fixed the output template with fields instead of free text and a "no data" flag if there is no support in the regulations. We mix into the prompt only relevant knowledge base fragments — the model stops completing facts on its own.
The share of incorrect answers fell from 38% to 6% on a control sample collected after fine-tuning. Manual review shrank to spot checks: the operator looks at low-confidence answers and disputed categories, while the rest goes to the client immediately. The accounting system accepts answers without field edits.
To keep the metric from drifting, we maintain a reference set and run it after every knowledge base update. If the share of errors exceeds the threshold, we analyze which wordings the model has not seen and add examples.
How does fine-tuning differ from prompt instructions and a knowledge base?
Three mechanisms cover different tasks. Instructions in the prompt control behavior here and now, document retrieval inserts current facts, and fine-tuning changes the model itself — for example, Gemini — its weights and tendency toward a certain response form.
If the model lacks knowledge — new regulations, nomenclature, operating conditions — fine-tuning is not needed.
Such data is faster and more reliable to provide through retrieval over an indexed base: it is updated without a repeat training run, and the answer can always be supported by a link to the source.
Fine-tuning retains facts unreliably and, on a small sample of consistent examples, produces an unstable result.
Fine-tuning is justified when the problem is not knowledge but behavior: you need a stable style and output structure, domain terminology, and a unified format for processing similar requests. Then the model reproduces the template itself, without a long description of rules in every prompt.
The method is chosen by the criterion: an error from not knowing a fact or from incorrect form. The first is covered by the knowledge base, the second by fine-tuning, and instructions set the frame over both.
In practice, the approaches are combined: the fine-tuned model holds the form, retrieval supplies fresh data, and instructions define the dialogue scenario and prohibitions.
We make the decision based on holdout measurements: if quality improves from instructions and retrieval, we do not launch training and do not spend time on it.
Technical concerns: data, security, version rollback
Concerns about fine-tuning usually come down to four questions: whether there is enough data, who is responsible for its quality, where it is stored, and whether it is possible to return to the previous model version.
Below are brief answers that we give before work starts: they determine the design of the training loop.
What volume of data is needed for fine-tuning?
The lower bound is several hundred labeled examples per task type; with 1–2 thousand, the model confidently holds wordings that were not in the set. If there are fewer examples, fine-tuning gives no gain over precise instruction tuning — this is visible on a pilot sample.
Who labels the examples and how?
Labeling is performed by subject-matter experts according to a written regulation. Before training, we calculate the disagreement rate between labelers: above the threshold, the rules are rewritten rather than "smoothed over" by majority.
How is sensitive data isolated?
The training loop is deployed within the customer's perimeter or in a dedicated closed project: datasets do not leave the loop, access is role-based, and accesses are logged. Only anonymized fragments go outside, if they are needed at all.
Is the result reproducible, and how do you roll back to the previous version?
Each run is recorded: dataset version, training parameters, control metric on the holdout set. Weights are stored as versioned artifacts — the previous model is brought up in minutes while the new one undergoes acceptance.
Let's discuss the task: what is needed for a start and an estimate
To analyze a fine-tuning task substantively, you do not need access to your production systems and the entire data archive — a description plus a small slice of examples is enough.
From them it is already clear whether tuning on the existing model is sufficient, which method of data provision will work, and where risks of overfitting on a narrow set will appear.
- Goal in terms of outcome: what the model should do differently than now, and how you measure it — manually or with an automated test.
- 50–200 live examples of "input → expected answer" exactly in the form in which they come from your system.
- Data provenance: whether labeling exists, who its author is, how a conflict of identical inputs with different answers is resolved.
- Current configuration: which Gemini branch is used, with what generation parameters and system behavior description.
- Output constraints: required fields in the answer, prohibited wordings, language and tone requirements.
- Acceptance scenario: the set of examples on which you will accept the work, and the permissible error share on it.
- Operating conditions: where the model runs, what request volume it handles, and whether there are response time requirements.
This can be collected directly in correspondence — a ready-made technical specification is not required at the start.
Describe the task, we will discuss it with engineers, and we will return with an estimate, a fine-tuning work plan, and a list of what will be needed from your teams.