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1Fix the driver behind crashes, sound loss and screen glitches2Repair Windows errors before they cause bigger problems3Scan for outdated or missing drivers - takes under a minuteScikit-LLM lets you build a text classifier with the familiar scikit-learn pattern of fit() and predict(), while a language model decides the label from a small set of labelled examples. The standard few-shot estimator does not fine-tune model weights: fit() stores your examples as prompt memory, and each prediction sends the relevant prompt to the model.
For a small, carefully designed label set, start with FewShotGPTClassifier. Keep the prompt memory compact—Scikit-LLM’s package documentation recommends no more than 10 samples per label—and permute example order to reduce recency bias. When the complete training corpus cannot fit in one context window, switch to DynamicFewShotGPTClassifier, which vectorizes the examples and retrieves nearby examples for each query.
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What few-shot classification means in Scikit-LLM
Few-shot text classification assigns an input text to one of a predefined set of classes by showing the model a small number of labelled examples. You provide pairs such as a support-ticket sentence and its label, then ask the model to classify new text using those demonstrations.
Scikit-LLM exposes this workflow through estimators that resemble scikit-learn models:
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fit(X, y)receives texts and their labels and builds the estimator’s few-shot prompt memory.predict(X_new)sends new texts to the configured language-model backend and returns predicted labels.
This is prompt-based inference, not conventional statistical training. As Iván Palomares Carrascosa explained in an October 14, 2026 KDnuggets article, “fit() does not perform actual training (weight updating) on the LLM.” It prepares the labelled examples the model will use for reasoning. The model’s weights remain unchanged, so a larger example set increases prompt length, token consumption and inference time rather than increasing a local training cost.
Choose the estimator before preparing data
The size and shape of your labelled data determine which estimator is appropriate. Use a fixed prompt when every example can fit comfortably in one request; use dynamic retrieval when the corpus is too large or when only a small, relevant subset should accompany each query.
| Estimator | How examples are selected | Best fit | Important trade-off |
|---|---|---|---|
FewShotGPTClassifier |
Every example supplied to fit() is included in the prompt memory. |
Small, stable datasets and the simplest implementation. | Prompt size and token use grow with the entire example set. |
DynamicFewShotGPTClassifier |
For each query, a vectorizer and nearest-neighbor memory index retrieve similar examples, with retrieval balanced by class. | Larger corpora that cannot fit in one context window. | Adds vectorization, indexing and retrieval behavior that must be configured and tested. |
MultiLabelFewShotGPTClassifier |
Uses few-shot demonstrations while allowing more than one label for an input. | Texts that legitimately belong to several classes. | You must set a deliberate maximum number of labels. |
The dynamic classifier’s documented default is three retrieved examples per class. That number is a retrieval setting, not an accuracy guarantee; change it only after checking context size, token use and validation results.
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Few-shot prompting works best when the model has a closed choice rather than an open-ended request to invent categories. Define the labels before writing examples and make each class distinguishable from the others.
1. Write the class definitions
Give every class a short operational definition that a human annotator could apply. If “access” means authentication and account-entry problems while “software” means application behavior after login, state that boundary explicitly. Avoid overlapping labels such as “technical issue” and “software issue” unless you can define when each applies.
2. Cover every class
Each label should have representative examples in the data passed to fit(). A class with no demonstrations has no prompt evidence, so the model has to guess from the label name alone.
3. Use realistic, concise examples
Examples should resemble the language seen at prediction time, including abbreviations, spelling variation and the amount of context users normally provide. Remove examples whose correct label depends on information that will not be present in production.
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4. Keep the prompt memory deliberately small
The Scikit-LLM package documentation recommends up to 10 samples per label for a training set that must fit in a single prompt. Treat this as a prompt-size recommendation, not a performance benchmark. More demonstrations consume context and tokens; they do not automatically improve decisions.
5. Hold out validation data
Separate a development or validation portion before finalizing the prompt memory. Do not feed an uncontrolled full corpus into the standard estimator and then evaluate on those same texts. A held-out set lets you detect ambiguous labels, prompt-order effects and systematic failures.
Install Scikit-LLM and configure a backend
Install Scikit-LLM in the Python environment used for your project, then pin the package and model settings for reproducibility. A typical installation command is:
python -m pip install scikit-llmKeep API credentials outside source control and outside notebooks that will be shared. Configure the credential mechanism required by your selected provider in the environment or secret manager used by your application.
The GPT family is the default Scikit-LLM backend family and follows OpenAI-compatible API formats. Backend selection is explicit in the model string:
| Backend | Model argument form | Configuration note |
|---|---|---|
| OpenAI-compatible default | model="gpt-4o" |
Uses the default GPT-family backend. |
| Azure deployment | model="azure::<deployment_name>" |
Replace the deployment placeholder with your Azure deployment name and configure Azure credentials and endpoint settings. |
| Custom OpenAI-compatible provider | model="custom_url::<model_name>" |
Set the provider’s custom URL through Scikit-LLM’s documented configuration before creating the estimator. |
Use a model identifier that is available to your account and keep the identifier fixed when comparing validation runs. Different models can follow the same prompt differently, so changing the model is a substantive experiment, not a harmless implementation detail.
Build a fixed few-shot classifier
The fixed estimator is the shortest path from labelled text to predictions. The following example uses three illustrative classes and the documented import path.
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from skllm.models.gpt.classification.few_shot import FewShotGPTClassifier
X_train = [
"The screen flickers when I open the app.",
"I cannot sign in after resetting my password.",
"The export finishes with a corrupted file.",
]
y_train = ["hardware", "access", "software"]
clf = FewShotGPTClassifier(model="gpt-4o")
clf.fit(X_train, y_train)
predictions = clf.predict([
"The login page rejects my new password."
])
print(predictions)Here, fit() records the three text-label pairs as prompt memory. It does not update gpt-4o or create a local model file. The call to predict() sends the new login complaint with those demonstrations and returns the estimator’s label output.
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For a real project, keep examples used to construct prompt memory separate from examples used to measure behavior. A simple split can be represented as two lists:
X_prompt = [
"The screen flickers when I open the app.",
"I cannot sign in after resetting my password.",
"The export finishes with a corrupted file.",
]
y_prompt = ["hardware", "access", "software"]
X_validation = [
"My new password is refused on the sign-in screen.",
"The downloaded report contains unreadable data.",
]
y_validation = ["access", "software"]
clf = FewShotGPTClassifier(model="gpt-4o")
clf.fit(X_prompt, y_prompt)
validation_predictions = clf.predict(X_validation)
for text, expected, predicted in zip(
X_validation, y_validation, validation_predictions
):
print({"text": text, "expected": expected, "predicted": predicted})The examples above are illustrative guidance, not a reported benchmark. For a meaningful evaluation, use enough held-out cases to cover each class and its confusing boundaries, then inspect errors rather than relying on a single aggregate number.
Control prompt order and example quality
Language models can show recency bias: demonstrations near the end of a prompt may receive disproportionate influence. Scikit-LLM’s official guidance recommends permuting the order of examples to reduce that effect.
Permute before fitting
Shuffle the prompt examples with a recorded seed, or evaluate several fixed permutations and choose an order based only on development data. Record the order alongside the model identifier so another run can be reproduced.
import random
paired = list(zip(X_prompt, y_prompt))
rng = random.Random(17)
rng.shuffle(paired)
X_prompt_shuffled, y_prompt_shuffled = zip(*paired)
clf = FewShotGPTClassifier(model="gpt-4o")
clf.fit(list(X_prompt_shuffled), list(y_prompt_shuffled))Do not shuffle the validation labels independently of their texts. The permutation applies to the prompt-memory pairs only.
Remove contradictory demonstrations
If near-identical texts carry different labels, the model cannot infer a stable boundary. Resolve the annotation disagreement or document the additional feature that distinguishes the cases. Prompt memory should teach a rule, not present unresolved contradictions.
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Watch the context budget
Every demonstration contributes input tokens on every fixed-estimator prediction. As the prompt grows, requests become slower and more expensive, and a provider’s context limit can be reached before the model sees the new text. The “up to 10 samples per label” recommendation is therefore a practical ceiling for the standard workflow, not a target that must be filled.
Use dynamic few-shot classification for larger corpora
When a complete labelled corpus will not fit in one prompt, use DynamicFewShotGPTClassifier. Instead of including every demonstration in every request, the dynamic pipeline vectorizes the examples, stores them in a nearest-neighbor memory index and retrieves similar examples for the incoming text. Retrieval is balanced by class so that one large class does not crowd every other class out of the prompt.
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from skllm.models.gpt.classification.few_shot import (
DynamicFewShotGPTClassifier,
)
X_train = [
"The screen flickers when I open the app.",
"I cannot sign in after resetting my password.",
"The export finishes with a corrupted file.",
"The monitor goes black during startup.",
"The account page says my password is invalid.",
"The generated archive cannot be opened.",
]
y_train = [
"hardware",
"access",
"software",
"hardware",
"access",
"software",
]
clf = DynamicFewShotGPTClassifier(
model="gpt-4o",
n_examples=3,
)
clf.fit(X_train, y_train)
predictions = clf.predict([
"The display turns black whenever I launch the program."
])n_examples=3 reflects the documented default of three nearest examples per class; specifying it explicitly makes the intended prompt size visible in your code. The exact retrieved texts depend on the vectorizer, index and corpus. Persist those settings, or rebuild them deterministically, when reproducibility matters.
Fixed versus dynamic decisions
| Decision axis | Fixed estimator | Dynamic estimator |
|---|---|---|
| Context-window fit | All supplied examples must fit together. | Only retrieved examples are sent for each query. |
| Token consumption | Rises with the full prompt-memory size on every prediction. | Controlled by the retrieved-example count, plus retrieval overhead outside the model request. |
| Retrieval complexity | No nearest-neighbor stage. | Requires vectorization and a memory index. |
| Class balance | Whatever balance you place in the prompt. | Documented retrieval balances examples by class. |
| Reproducibility | Depends mainly on model behavior and prompt order. | Also depends on vectorizer, index, corpus and tie-breaking behavior. |
Dynamic retrieval is not a guarantee that the semantically best demonstration will be selected. Inspect retrieved examples during development and test cases near class boundaries. If retrieval repeatedly omits a rare class, improve its examples or adjust the retrieval configuration rather than simply adding unrelated data.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Handle multilabel text deliberately
Use MultiLabelFewShotGPTClassifier when one text can validly receive several labels. Set max_labels to the maximum number of labels your application can accept:
from skllm.models.gpt.classification.few_shot import (
MultiLabelFewShotGPTClassifier,
)
clf = MultiLabelFewShotGPTClassifier(
model="gpt-4o",
max_labels=2,
)
clf.fit(X_train, y_train)
predictions = clf.predict(X_new)The maximum is a policy decision. If your downstream system supports only one or two categories, enforce that limit in the estimator and validate cases where a text appears to contain several issues. Do not use the multilabel estimator merely because labels have similar names; use it when multiple labels are genuinely correct at the same time.
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Validate predictions before production use
A few-shot classifier can return plausible but incorrect labels, especially when classes overlap or the input is outside the examples’ domain. Build a validation loop that examines both aggregate behavior and individual errors.
Check every class
- Include validation texts for every label, including the least frequent class.
- Add boundary cases that differ by one important detail, such as “cannot sign in” versus “exported file is corrupted.”
- Include short, noisy and unusually worded inputs if those occur in production.
- Record the model identifier, prompt examples, permutation seed and estimator settings for each run.
Inspect failures by cause
When a prediction is wrong, determine whether the problem is an ambiguous taxonomy, a misleading demonstration, a missing class example, retrieval failure or model uncertainty. Rewriting one example can be more effective than adding many redundant examples, because the prompt should express the class boundary clearly.
Do not treat a prompt recommendation as an accuracy claim
Scikit-LLM documentation provides configuration guidance such as up to 10 samples per label for fixed prompting and three retrieved examples per class by default for dynamic prompting. No authoritative, general accuracy, latency or price statistic is established for this workflow. Measure those properties on your own texts, model, provider and request settings.
Operate the classifier safely and reproducibly
Protect credentials and sensitive text
API credentials belong in environment variables or a secret manager, never in committed Python files. Review whether support tickets, emails or other inputs contain personal or confidential information before sending them to an external model endpoint. Apply your organization’s retention and access policies to prompts, responses and logs.
Pin the moving parts
Record the Scikit-LLM package version, model string, backend configuration, estimator type, prompt examples, example order, dynamic vectorizer/index settings and n_examples or max_labels. A change to any of these can change predictions even when the input text is identical.
Control token and latency growth
- For fixed prompting, remove duplicate or low-information demonstrations and keep within the documented small-prompt guidance.
- For dynamic prompting, begin with the default three examples per class and increase only when validation shows a clear benefit.
- Batch predictions only if your provider and application code support it without mixing inputs or labels.
- Track request failures, rate limits and malformed responses separately from classification errors.
Keep a fallback path
Decide what your application should do when the model endpoint is unavailable, a response cannot be parsed or the text does not resemble any known class. A safe fallback can route the item to manual review or an explicit “unknown” workflow, provided that state is part of your application design rather than an invented classifier label.
Troubleshoot common problems
Import error for the classifier
Verify that Scikit-LLM is installed in the same Python environment running the script and that the import path matches the package documentation: skllm.models.gpt.classification.few_shot. Check the installed package version before changing code.
Authentication or endpoint failure
Confirm that the selected backend matches the configured credentials. A default GPT-family model, an Azure deployment string and a custom OpenAI-compatible URL use different endpoint settings. Keep the model argument and endpoint configuration together in your deployment configuration.
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Reduce the fixed prompt memory, shorten unnecessarily verbose demonstrations or migrate to DynamicFewShotGPTClassifier. For the dynamic estimator, lower n_examples if the retrieved prompt is still too large.
One class dominates predictions
Check whether that class has more or more distinctive demonstrations, whether examples for other classes are vague, and whether retrieval is balanced as expected. Rewrite overlapping labels and inspect the dynamic classifier’s selected neighbors.
Predictions change after harmless-looking edits
Compare the model identifier, package version, prompt order, examples, backend and retrieval settings. Because the model reasons from prompt memory, changing example order or wording can alter results even when the label set is unchanged.
Quick Recap
A practical build checklist
- Define a closed set of non-overlapping labels and write a short rule for each.
- Create representative labelled examples for every class.
- Split development examples from a held-out validation set.
- Choose
FewShotGPTClassifierwhen the complete prompt memory is small; chooseDynamicFewShotGPTClassifierwhen the corpus is too large for one context window. - Configure the OpenAI-compatible, Azure or custom backend explicitly and keep credentials out of source code.
- Instantiate the estimator with a pinned model identifier.
- Call
fit(X_train, y_train)to construct prompt memory or the dynamic index. - Permute fixed examples and record the seed to reduce recency bias and preserve reproducibility.
- Call
predict(X_new)on held-out data and inspect errors by class boundary. - For multilabel tasks, use
MultiLabelFewShotGPTClassifierand setmax_labelsdeliberately. - Monitor context size, token consumption, latency, endpoint errors and fallback volume after deployment.
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