> ## Documentation Index
> Fetch the complete documentation index at: https://docs.athina.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# Quick Start

There are many ways to use Athina Evals.

## Running Evals in UI

1. **[Run evals on datasets](datasets/run-eval)**
2. **[Configure online evals](evals/online-evals) to run automatically:** These will run automatically on your logged inferences, and you can view the results in the dashboard.
3. **Run evaluations manually from the [Trace view](/monitoring/inference-trace)**
4. **Run evaluations in the Prompt playground**
5. **Configure **[Automations](/datasets/automations) to run evals automatically on new datasets** in a project**

***

## Run Evals Programmatically[](#run-evals-programmatically)

1. **[Run evals in CI / CD pipelines](evals/running-evals-ci-cd)**
2. **[Run evals as guardrails around inference](guides/evals/running-evals-guardrails)**
3. **[Run evals using the Python SDK](api-reference/evals/running-evals/run-eval-suite)**

Here's a quickstart guide for running evals using the Python SDK:

**1. Install the `athina` package**

```python
pip install athina
```

**2. Set your API keys**

If you are using the python SDK, then can set the API keys like this:

```python
from athina.keys import AthinaApiKey, OpenAiApiKey

OpenAiApiKey.set_key(os.getenv('OPENAI_API_KEY'))
AthinaApiKey.set_key(os.getenv('ATHINA_API_KEY'))
```

If you are using the CLI, then run `athina init`, and enter the API keys when prompted.

**3. Load your dataset like this:**

*You can also [load data](api-reference/evals/loading-data/loading-data-for-eval) using a Python Dictionary*

```python
from athina.loaders import RagLoader

dataset = RagLoader().load_json(json_filepath)
```

**4. Now you can run evals like this.**

```python
from athina.evals import DoesResponseAnswerQuery

DoesResponseAnswerQuery().run_batch(data=dataset)
```

For more details, see this guide on [running evals](/evals/overview).