Quickstart Tutorials

The Cohere API is created to help you build natural language understanding and generation into your production with a few lines of code. Our Quickstart Tutorials will show you how to implement our API from zero-to-one in under 5 minutes.

  1. Customer support tickets can come from all directions, and manually analyzing and routing information is an overwhelming job. A text classification system can help support teams accelerate this process.

    Here is an example of classifying customer emails to an insurance company into four categories: Finding policy details, Change account settings, Filing a claim and viewing status, and Cancelling coverage.
  1. Chatbots are designed to understand and respond to human language. They need to be able to understand the text they hear and understand the context of the conversation. They also need to be able to respond to people’s questions and comments in a meaningful way. To accomplish this, chatbots must be able to recognize specific intents that people express in conversation.

    Here is an example of classifying the intent of customer inquiries on an eCommerce website into three categories: Shipping and handling policy, Start return or exchange, or Track order.

  1. Sentiment analysis is a type of classification task that analyzes the tone of a piece of text. It is used in a variety of different ways, such as for social media comments and customer reviews. It is commonly used to see how people feel about their products or company, but it can also be used to help businesses understand how different trends in the economy may impact their business.

    Here is an example of classifying the sentiments of customer feedback about a product into three categories: Positive, Negative, or Neutral.
  1. The goal of text summarization is to condense the original text into a shorter version that retains the most important information. In this example, we want to summarize a passage from a news article into its main point.
  1. The internet is dominated by user-generated content. While this provides an avenue for online platforms to grow, it is a bane for the content moderators managing them because it is impossible for humans to manually moderate all the user content that is created. This is why an automated solution is needed for flagging toxic content.

    Here is an example of classifying online user comments for toxicity into two categories: Toxic or Not Toxic.