apps/opik-documentation/documentation/fern/docs/prompt_engineering/playground.mdx
When working with LLMs, there are times when you want to quickly try out different prompts and see how they perform. Opik's prompt playground is a great way to do just that.
The prompt playground is a simple interface that allows you to enter prompts and see the output of the LLM. It allows you to enter system, user and assistant messages and see the output of the LLM in real time.
You can also easily evaluate how different models impact the prompt by duplicating a prompt and changing either the model or the model parameters.
<Frame> </Frame>All of the conversations from the playground are logged to the playground project so that you can easily refer back to them later.
The playground supports the following LLM providers:
Go to configuring AI Providers to learn how to configure the prompt playground.
You can evaluate prompts in the playground by using variables in the prompts using the {{variable}} syntax. You can then connect a dataset and run the prompts on each dataset item. This allows both technical and non-technical users to evaluate prompts quickly and easily.
When using datasets in the playground, you need to ensure the prompt contains variables in the mustache syntax ({{variable}}) that align with the columns in the dataset. For example if the dataset contains a column named user_question you need to ensure the prompt contains {{user_question}}.
Once you are ready to run the experiment, simply select a dataset next to the run button and click on the Run button. You will then be able to see the LLM outputs for each sample in the dataset.
If a dataset column contains JSON-formatted content, you can use dot notation to reference nested values directly when querying or filtering. Dot notation lets you specify the path to a nested field (e.g., {{input.user.name}}) to extract only the value you need from within a structured object. This makes it easier to work with deeply nested data without manual flattening or custom parsing.
The playground supports multimodal prompts with images when using vision-capable models. You can add images in two ways:
You can add images directly to your prompt messages through the playground UI:
<Frame> </Frame> <Note> **Internal representation**: When you add an image through the UI, Opik internally stores it with `<<<image>>><</image>>>` wrapper tags on a new line in the prompt. This internal format is not visible in the UI but ensures proper serialization and processing of multimodal content. </Note>When evaluating prompts with datasets, you can reference image columns using the standard Mustache syntax:
Analyze this product image: {{product_image}}
Where product_image is a column in your dataset containing image data.
Supported image formats: