> For the complete documentation index, see [llms.txt](https://docs.bespokelabs.ai/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.bespokelabs.ai/bespoke-curator/getting-started/quick-tour.md).

# Quick Tour

## Installation

```
pip install bespokelabs-curator
```

## Hello World with LLM

The `LLM`class provides a flexible interface to generate data with LLMs.  Below is a minimal example of using `LLM`: we simply create an `LLM` object with a `model_name`, in this case `gpt-4o-mini`, and passing in a prompt.

```python
from bespokelabs import curator
llm = curator.LLM(model_name="gpt-4o-mini")
poem = llm("Write a poem about the importance of data in AI.")
print(poem.to_pandas())
# Output:
#                                             response
# 0  In the realm where silence once held sway,  \n...

# Or you can pass a list of prompts to generate multiple responses.
poems = llm(["Write a poem about the importance of data in AI.",
            "Write a haiku about the importance of data in AI."])
print(poems.dataset.to_pandas())
# Output:
#                                             response
# 0  In the realm where silence once held sway,  \n...
# 1  Silent streams of truth,  \nData shapes the le...
```

### What's next?

* Check out the key concepts of the library in [Key Concepts](/bespoke-curator/getting-started/key-concepts.md)
* See  important caching feature avail