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You can build a working few-shot text classifier with Scikit-LLM by defining a closed set of labels, giving FewShotGPTClassifier a small labeled training set, and then calling the familiar scikit-learn-style fit() and predict() methods. The estimator does not fine-tune model weights: fit() assembles a prompt memory containing your examples, and the language model uses that memory at inference time.
For a small, carefully selected dataset, use the fixed classifier. When the complete example set will not fit comfortably in one context window, use DynamicFewShotGPTClassifier to retrieve similar examples for each query. This guide shows both workflows, how many examples to provide, how to configure GPT-compatible backends, and how to validate the resulting predictions.
What few-shot classification means in Scikit-LLM
Few-shot text classification assigns each input to one of a predefined set of classes after showing the model a small number of labeled examples. Instead of updating the model, you describe the task through examples such as an input sentence paired with its intended label. Scikit-LLM exposes that pattern through estimators that resemble scikit-learn models.
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In the standard workflow, your labeled examples become prompt memory. A new text is placed into a prompt containing those examples, and the GPT-family backend generates the predicted class. The model therefore learns the mapping for the current call from context; it is not permanently changed by your dataset.
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What the fixed estimator does
FewShotGPTClassifier stores every example supplied to fit(X, y). During predict(X_new), those examples are included in the request so the model can infer which label best matches each new text. This is straightforward and reproducible when the memory is small enough to fit in the model’s context window.
What it does not do
fit() does not perform gradient updates, create a new model checkpoint, or alter the provider’s model weights. As Iván Palomares Carrascosa explained in KDnuggets on October 14, 2026, it “simply builds a few-shot prompt memory for the model, containing the labeled examples it will rely on for reasoning and generating its responses.” The practical consequences are important: adding examples increases prompt length, inference time, and token consumption, but does not create a separately trained classifier.
Plan the labels and examples before writing code
The quality of a few-shot classifier depends more on the label design and examples than on the number of lines in the Python program. Treat the label set as a closed contract: every input should have one of the labels you define, unless you intentionally add an “other” or “unknown” class.
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Use non-overlapping class definitions
- Give each class a short name with a single operational meaning.
- Write a description or internal rule for deciding between classes that are easy to confuse.
- Use examples that resemble the language, spelling, and length of production inputs.
- Include boundary cases, not only obvious examples, so the model sees how neighboring classes differ.
- Keep labels consistent in spelling and capitalization; a label is part of the model’s output contract.
How many labeled examples per class?
Scikit-LLM’s package documentation recommends a training set of no more than 10 samples per label for the standard few-shot estimator, because the complete set must fit into a single prompt. That number is a context-and-cost guideline, not an accuracy guarantee. Ten nearly identical examples can be less useful than a smaller set covering different phrasings and edge cases.
Reserve some labeled data for validation before you finalize the prompt memory. If every available example is placed in the memory, you cannot tell whether the classifier generalizes to unseen wording. A simple split by class gives you a development portion for prompt construction and a held-out portion for checking predictions.
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Permute example order
The documentation advises permuting the order of examples to reduce recency bias. If you repeatedly place one class’s examples at the end of the prompt, the model may over-predict that class because of position rather than meaning. Shuffle the order before fitting, and keep the resulting order fixed when you compare experiments.
Build a fixed few-shot classifier
The minimal implementation uses the documented import path and the familiar estimator interface. The labels below are illustrative categories, not a benchmark or a claim about model accuracy.
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)The strings in X_train and y_train must align by position: the first text has the first label, and so on. After fit(), pass one or more unseen texts to predict(). Keep the returned labels as data and compare them with your held-out labels rather than assuming that a syntactically valid response is correct.
Step-by-step implementation
- Define the label list. Decide exactly which classes are legal outputs and document the distinction between similar classes.
- Prepare representative examples. Select a small, balanced set for every class. Remove duplicates, contradictory labels, and examples that reveal information unavailable at prediction time.
- Split before fitting. Keep a validation portion aside so it never enters the prompt memory used for the first evaluation.
- Configure a backend. Select the GPT-compatible model and supply credentials through your runtime’s secret-management mechanism rather than embedding keys in source code.
- Instantiate the estimator. Pass the model identifier to
FewShotGPTClassifier(model=...). - Build prompt memory. Call
fit(X_train, y_train). This organizes examples for prompting; it does not train model weights. - Predict and inspect. Call
predict(X_new), then review errors against the held-out labels and revise definitions or examples deliberately. - Freeze the experiment. Record the package version, model identifier, example order, label definitions, and backend configuration so a later run is comparable.
Choose and configure the GPT backend
The GPT classifier family uses an OpenAI-compatible API format and OpenAI as its default backend. Scikit-LLM also documents Azure deployments and custom OpenAI-compatible providers.
Default OpenAI-compatible usage
Pass the model name when constructing the classifier, as in FewShotGPTClassifier(model="gpt-4o"). Configure the provider’s credentials in the environment or secret store used by your application. Do not commit credentials to a notebook, source repository, or serialized prompt-memory file.
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Azure deployments
For an Azure deployment, use the documented model naming form azure::<deployment_name>. The deployment name is the identifier configured in your Azure account, not necessarily the public model name. Keep the endpoint, API version, and credentials in your deployment configuration and test them independently before diagnosing classification behavior.
Custom OpenAI-compatible endpoints
For another provider that implements the compatible API format, set the custom URL through Scikit-LLM’s documented configuration and use custom_url::<model_name> as the model identifier. Pin the URL and model name in your experiment configuration; changing either can change token limits, output behavior, and latency.
Fixed versus dynamic few-shot selection
The fixed estimator includes every supplied example on every prediction. The dynamic estimator selects a smaller, query-specific subset, which is useful when your corpus is too large for one context window.
| Aspect | FewShotGPTClassifier |
DynamicFewShotGPTClassifier |
|---|---|---|
| Examples sent for a query | Every example supplied during fit() |
Nearest examples retrieved for that query |
| Best fit | Small, curated prompt memory | Large example collections that cannot fit in one prompt |
| Context and token use | Grows with the full example set | Controlled by the configured examples-per-class value |
| Selection mechanism | None; the supplied order is used | Vectorization plus a nearest-neighbor memory index |
| Documented default | Not stated | Three nearest examples per class |
| Trade-off | Simpler and easier to reproduce | More retrieval machinery and query-dependent prompts |
Use dynamic few-shot classification for larger corpora
DynamicFewShotGPTClassifier builds a memory index from your examples. Its documented pipeline vectorizes the texts, finds nearest neighbors for each new query, and balances retrieval by class. Rather than placing the entire corpus into every request, it selects the most similar examples for each label.
Dynamic classifier pattern
from skllm.models.gpt.classification.dynamic_few_shot import (
DynamicFewShotGPTClassifier,
)
clf = DynamicFewShotGPTClassifier(
model="gpt-4o",
n_examples=3,
)
clf.fit(X_train, y_train)
predictions = clf.predict(X_new)The documented default is three retrieved examples per class. Set n_examples explicitly when you need a different prompt budget, and keep that value in your experiment record. The total number of retrieved examples grows with the number of classes, so a high value can still exceed a context limit when the label set is large.
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How retrieval changes the workflow
- Index the labeled corpus. During
fit(), the classifier vectorizes the example texts and constructs its nearest-neighbor memory. - Query the index. For each new text, the vectorizer represents the query in the same feature space.
- Retrieve by similarity. The memory returns nearby examples rather than the entire corpus.
- Balance by class. The documented pipeline retrieves examples per class so a class with many more records does not automatically dominate the prompt.
- Generate the label. The selected examples are inserted into the model prompt and the backend predicts the class.
Retrieval improves context-window fit, but it introduces another source of behavior: the vectorizer, distance calculation, memory index, and tie handling can affect which examples are selected. Save those settings and the training data version if you need reproducibility.
Multilabel classification
Use MultiLabelFewShotGPTClassifier when one text may legitimately receive several labels. Set max_labels deliberately; it limits how many labels the estimator may return. Your examples must demonstrate the allowed combinations and the difference between one applicable label and several applicable labels.
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)Do not use the multilabel estimator merely because your labels have similar names. Choose it when multiple labels are valid for the same input and downstream code can consume a collection of labels.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Validate predictions instead of trusting prompt fit
A successful call only proves that the backend returned an output. It does not establish classification quality. Evaluate predictions on examples that were withheld before fit().
Validation checklist
- Check that every predicted value belongs to the allowed label set.
- Count errors by class, not only overall correctness; a dominant class can hide failures in smaller classes.
- Inspect confusion between neighboring labels and add contrastive examples where the distinction is unclear.
- Test paraphrases, misspellings, short inputs, and the longest realistic inputs.
- Repeat a small evaluation after permuting example order to detect sensitivity to recency.
- For dynamic retrieval, inspect which examples were selected for difficult queries and verify that they are semantically relevant.
Keep a reproducible evaluation record
Record the package version, model and backend identifier, label definitions, training and validation split, example order, dynamic retrieval settings, and date of the run. Also record prompt or token limits exposed by your chosen provider. These details explain changes that otherwise look like random classifier drift.
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Control context size, latency, and token consumption
Every additional fixed example is inserted into the prompt, so a larger memory consumes more input tokens and can increase inference time. A prompt that fits today may fail after adding classes, lengthening examples, or switching to a model with a smaller context window.
When the fixed memory is appropriate
- The number of classes is modest.
- You can curate a compact set of high-information examples.
- You want the same examples sent for every query.
- The complete prompt remains within the selected model’s context and operational budget.
When to move to dynamic retrieval
- The labeled corpus is too large to include in one request.
- Many examples are redundant, but relevant examples vary by query.
- You need a bounded prompt budget while retaining a larger searchable memory.
- You can test and monitor retrieval quality as well as model predictions.
Dynamic selection does not remove token costs; it limits the examples sent per request. Retrieval itself also adds preprocessing and maintenance work.
Troubleshoot common failure modes
The model returns an unexpected label
Check for overlapping definitions, inconsistent capitalization, and examples whose text supports a different class than their assigned label. Add a clearly labeled boundary example and rerun validation. If the backend emits explanatory text instead of a legal label, enforce the output contract in your application before accepting the result.
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Look for class imbalance in the fixed prompt and examples that are more recent or more verbose than the others. Rebalance the curated set, permute its order, and evaluate per-class errors. With dynamic retrieval, confirm that examples are being retrieved per class as documented rather than allowing the largest class to fill the prompt.
The request exceeds the context window
Reduce the fixed example set, shorten redundant examples without removing their distinguishing information, or switch to DynamicFewShotGPTClassifier with an explicit n_examples value. Also account for the query text and instruction overhead, not only the examples.
Dynamic results seem unrelated
Inspect the vectorizer and memory-index configuration, then examine the neighbors selected for representative queries. If the corpus contains boilerplate, remove it or normalize the text before fitting. Add examples using vocabulary that appears in real queries; nearest-neighbor retrieval cannot recover distinctions that are absent from the feature representation.
Results change after a backend change
Different deployments can have different model versions, context limits, tokenization, and response behavior. Treat a backend or model identifier change as a new experiment, rerun the held-out evaluation, and keep the old configuration available for rollback.
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- Define and version the closed label set.
- Review examples for contradictory or sensitive content before they enter prompt memory.
- Split validation data before calling
fit(). - Keep credentials in a secret manager or environment configuration.
- Pin the Scikit-LLM package, model identifier, backend settings, and retrieval parameters.
- Log predictions, confidence signals available from your application, latency, and token usage without storing unnecessary personal data.
- Route invalid or ambiguous outputs to a review path instead of silently assigning a class.
- Monitor drift by periodically evaluating newly labeled production samples.
Practical decision guide
Start with FewShotGPTClassifier when you have a small, curated set and want the simplest scikit-learn-style implementation. Keep the memory compact, permute its order, and validate on held-out data. Choose DynamicFewShotGPTClassifier when the full labeled corpus cannot fit in one context window or when query-specific examples are more useful than a single universal prompt. Use MultiLabelFewShotGPTClassifier only when multiple labels can be correct for one input, and set max_labels to the largest valid output.
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