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The Playground lets you create and iterate on prompts without writing code. Use it to:
  • Experiment with prompt templates and model configurations
  • Test prompts against sample inputs before deploying
  • Save working versions for use in your application
HoneyHive Playground interface with provider selection, chat template editor, and response output

Playground interface showing prompt template, model selection, and test output

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Prerequisites

Before using the Playground, configure your model provider API keys in Settings > AI Provider Secrets.
You can configure multiple providers (OpenAI, Anthropic, etc.) and switch between them in the Playground.

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Creating a Prompt

  1. Navigate to Studio > Playground in the sidebar
  2. Select a Provider and Model in the left panel
  3. Write your prompt template in the Chat Template section
  4. Use {{variable}} syntax for dynamic inputs (e.g., {{question}})
  5. Add sample values in the Inputs panel
  6. Click Run to test the prompt

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Template Variables

Use {{variable_name}} syntax in your prompt template to create dynamic inputs. When you add a variable, an input field appears in the Inputs panel on the left where you can set sample values for testing. Variables are replaced with actual values at runtime when you fetch prompts in your code.

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Hyperparameters

Expand the Hyperparameters panel in the left sidebar to configure model parameters:
For OpenAI reasoning models (o1 series and similar), temperature, top_p, presence_penalty, and frequency_penalty are fixed and cannot be adjusted.

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Response Format

For OpenAI models that support JSON mode, you can set the response format to:
  • Text: Default. Free-form text response.
  • JSON: Forces the model to output valid JSON. Useful when you need structured output for downstream processing.
The response format option appears in the Hyperparameters panel when a compatible OpenAI model is selected.

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Multi-Turn Conversations

The Playground supports multi-turn chat. After running a prompt:
  1. The model’s response appears in the Conversation panel
  2. Type a follow-up message and click Run again
  3. The full conversation history is sent with each request
This lets you test how your prompt handles multi-turn interactions before deploying.

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Saving and Forking

Prompts are saved as configurations - each configuration is a single record that you can update or fork.
To preserve a working prompt before experimenting, use Fork first. Saving an existing configuration overwrites it.
To save a prompt:
  1. Click Save in the top toolbar
  2. Enter a configuration name (e.g., v1-production)
  3. The saved configuration appears in Studio > Prompts
To create a variant without losing the original:
  1. Click Fork to create a copy
  2. Make your changes
  3. Save the forked version with a new name

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Managing Saved Prompts

View all saved prompts in Studio > Prompts:
All Prompts table showing environment badges (prod, staging, dev), version names, models, and prompt templates

Prompts list showing saved versions with deployment environment, model, and template preview

From here you can:
  • Deploy a prompt to an environment (dev, staging, prod)
  • Edit a prompt by opening it in the Playground
  • Compare different versions side-by-side

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Opening Prompts from Traces

When debugging production issues, you can open any traced LLM call in the Playground:
  1. Go to Traces and find the trace
  2. Click on a model event
  3. Click Open in Playground in the top right
This loads the exact prompt template, model, and hyperparameters from that production call so you can iterate on improvements.

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Sharing

To share a prompt with teammates:
  1. Save the prompt first
  2. Click Share in the top right
  3. Copy the link
Anyone on your team with access can view and fork the shared prompt.

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Next Steps

Deploy Prompts to Code

Fetch saved prompts in your application via SDK or YAML export.

Run Evaluations

Test prompt performance systematically with experiments.