> ## Documentation Index
> Fetch the complete documentation index at: https://arize-ax.mintlify.site/docs/llms.txt
> Use this file to discover all available pages before exploring further.
# Test prompts on datasets
> Experiment with your datasets in prompt playground
export const AskAlyx = ({children}) => {
const gradientId = `askAlyxGradient-${Math.random().toString(36).slice(2)}`;
return ;
};
When modifying a prompt in the playground, you can test your new prompt across a dataset of examples to validate that the model is **hill climbing** in terms of performance across challenging examples, without regressing on core business use cases.
**Ask Alyx** to do any of the following steps, or to do all of them at once!
# Step 1: Set a Dataset
**Ask Alyx** to attach a dataset to the playground for you.
1. Follow this guide to upload your dataset to Arize AX [Create a dataset](/docs/ax/develop/datasets/how-to-datasets)
2. Go back to the prompt playground, and choose your dataset from the **Select a Dataset** dropdown
# Step 2: Set your Prompt
**Ask Alyx** to load a prompt from Prompt Hub or write a new one for you.
1. Load your prompt from the Prompt Hub, using the **Select a template from prompt hub** dropdown
2. OR, fill in a new prompt (See more: [Create a Prompt](/docs/ax/prompts/prompt-hub/create-a-prompt))
3. Include variables from your dataset in the prompt, inside curly braces
## Step 3: Add Evaluators
**Ask Alyx** to add evaluators to this experiment for you.
Select **Add Evaluator** to add evaluators to evaluate outputs generated by this experiment.
## Add a Code Eval
Write a programmatic evaluator if you'd like to use code to judge your experiment outputs.
Learn more here: [Code Evaluations](/docs/ax/evaluate/evaluators/code-evaluations)
## Add an LLM as Judge Evaluator
Use an LLM to judge your experiment outputs. Learn more: [LLM as a Judge](/docs/ax/evaluate/evaluators/llm-as-a-judge)
1. Select one our Arize eval templates
2. OR write your own. Make sure to embed variabes from the dataset, so that the evaluator has something to evaluate
3. Set your eval labels. These are the labels the evaluator will pick from, when judging the output
4. Set explanations on/off. Explanations are short reasoning blobs that the LLM will generate to explain its reasoning. Also set advanced options.
5. Click **Create Eval** once you are done.
# Step 4: Run Experiment
**Ask Alyx** to run the experiment for you.
Once you hit **Run**, the experiment run will trigger.
Hit **View Experiment** to get a detailed view of your experiment run.
Hover over the eval label to see the eval explanation.