# 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


---

# 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://docs.bespokelabs.ai/bespoke-curator/getting-started/quick-tour.md?ask=<question>
```

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.