Classification

This guide walks through the process of determining the best approach for building a classifier with Claude and the essentials of end-to-end deployment for a Claude classifier, from use case exploration to back-end integration.

When to use Claude for classification

When should you consider using an LLM instead of a traditional ML approach for your classification tasks? Here are some key indicators:

1. Rule-based classes: Use Claude when classes are defined by conditions rather than examples, as it can understand underlying rules.
2. Evolving classes: Claude adapts well to new or changing domains with emerging classes and shifting boundaries.
3. Unstructured inputs: Claude can handle large volumes of unstructured text inputs of varying lengths.
4. Limited labeled examples: With few-shot learning capabilities, Claude learns accurately from limited labeled training data.
5. Reasoning Requirements: Claude excels at classification tasks requiring semantic understanding, context, and higher-level reasoning.

Establish your classification use case

Below is a non-exhaustive list of common classification use cases where Claude excels by industry.

Implement Claude for classification

The three key model decision factors are: intelligence, latency, and price.

For classification, a smaller model like Claude Haiku 3 is typically ideal due to its speed and efficiency. Though, for classification tasks where specialized knowledge or complex reasoning is required, Sonnet or Opus may be a better choice. Learn more about how Opus, Sonnet, and Haiku compare in the models overview.

Use evaluations to gauge whether a Claude model is performing well enough to launch into production.

1. Build a strong input prompt

While Claude offers high-level baseline performance out of the box, a strong input prompt helps get the best results.

For a generic classifier that you can adapt to your specific use case, copy the starter prompt below:

2. Develop your test cases

To run your classification evaluation, you will need test cases to run it on. Take a look at the guide to developing test cases.

3. Run your eval

Evaluation metrics

Some success metrics to consider evaluating Claude’s performance on a classification task include:

Deploy your classifier

To see code examples of how to use Claude for classification, check out the Classification Guide in the Claude Cookbook.