# What is Cohere playground? {% hint style="info" %} {% endhint %} It is a visual interface for users to test Cohere's large language models without a single line of code. To familiarize yourself with the endpoints, you are recommended to click the **Generate** or **Calculate** button on each endpoint and observing the outputs. Use the playground to test your use cases and when you're ready to start building, simply click **Export Code** to add Cohere's functionality to your application. The Cohere Platform Cohere offers an API to add cutting-edge language processing to any system. Cohere trains massive language and puts them behind a simple API. Moreover, through training, users can create massive models customized to their use case and trained on their data. This way, Cohere handles the complexities of collecting massive amounts of text data, the ever-evolving neural network architectures, distributed training, and serving models around the clock.

Cohere offers access to both generation models (through the generation endpoint) and representation models (through the embed endpoint which returns an embedding vector for the input text).

Two major categories of large language models are generative language models (like GPT2 and GPT3) and representation language models (BERT). Cohere offers variants of both types. ## Summarize Large language models present a breakthrough in text generation. For the first time in history, we have software programs that can write text that sounds like it's written by humans. These capabilities open doors to use cases like summarization or paraphrasing.

Language models can be instructed to generate useful summaries or paraphrases of input text by guiding them using a task description in the prompt.

A summarization prompt in the Cohere playground shows this output(in bold).

Example summarization prompt and generation. Stop sequence is specified as the period to limit the output to one sentence.

Large language models can be adapted to new tasks with impressive speed. For tasks which appear in the training data(i.e. documents on the web), language models can successfully summarize text without being shown any examples at all.

Two strategies you can experiment with generative language models are prompt engineering and training (which creates a custom model based on your dataset).

--- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter: ``` GET https://yunaeecy.gitbook.io/wiki/artificial-intelligence/what-is-cohere-playground.md?ask= ``` The question should be specific, self-contained, and written in natural language. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.