#    messages=[cohere.UserMessage(content=message)])

print(response.message.content[0].text)
```

```
Sure! Here is a draft of an introduction message: 

"Hi everyone! My name is [Your Name], and I am thrilled to be joining the Co1t team today. I am excited to get to know you all and contribute to the amazing work being done at this startup. A little about me: [Brief description of your role, experience, and interests]. Outside of work, I enjoy [Hobbies and interests]. I look forward to collaborating with you all and being a part of Co1t's journey. Let's connect and make something great together!" 

Feel free to edit and personalize the message to your liking. Good luck with your new role at Co1t!
```

Further reading:

* [Chat endpoint API reference](https://docs.cohere.com/v2/reference/chat)
* [Documentation on Chat fine-tuning](https://docs.cohere.com/docs/chat-fine-tuning)
* [Documentation on Command A](https://docs.cohere.com/docs/command-a)
* [LLM University module on text generation](https://cohere.com/llmu#text-generation)

## Prompt engineering

Prompting is at the heart of working with LLMs. The prompt provides context for the text that we want the model to generate. The prompts we create can be anything from simple instructions to more complex pieces of text, and they are used to encourage the model to produce a specific type of output.

In this section, we'll look at a couple of prompting techniques.

The first is to add more specific instructions to the prompt. The more instructions you provide in the prompt, the closer you can get to the response you need.

The limit of how long a prompt can be is dependent on the maximum context length that a model can support (in the case Command A, it's 256k tokens).

Below, we'll add one additional instruction to the earlier prompt: the length we need the response to be.

```python PYTHON