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Learn about Cohere's UI in the playground overview. Read Intro to Large Language Models with Cohere for a brief visual overview to Large Language Models (LLMs) and some of their applications.

Text Classification#

Text classification is one of the most useful applications of Large Language Models (LLMs). They can classify text using a small number of examples (few-shot classification).

See the text classification with Classify tutorial which demonstrates the Classify endpoint.

See the text classification with Embeddings tutorial which demonstrates the Embed endpoint.

See the content moderation with Classify tutorial which demonstrates the Classify endpoint.

Text Summarization#

The Summarization and paraphrasing article walks you through using the Generate endpoint for summarization. See how to build this capability into a Slack bot in the Building an Arxiv Paper Summarizer Slack App guide.

Semantic Search#

Learn how to use embeddings to build semantic search capabilities.

Entity Extraction#

Extract information from text using only a few examples.

Language Generation#

LLMs can write coherent text like no other human technology before them could. We tune the inputs using prompt engineering techniques that get the model to produce useful outputs. Important text generations parameters include top-k and top-p. For an example on how to include this capability in your web application, see the React Tutorial using Generate.


Customize Cohere models to fit your use case by finetuning our baseline models with your own data. Learn about finetuning generation models in addition to finetuning representation models.

Notebooks and code examples#

See the notebooks repo for code examples on common LLM use cases.


The Cohere platform is often used in pipelines alongside other tools and services.