Using the Playground
General guidelines on how to use Phoenix's prompt playground
Last updated
Was this helpful?
General guidelines on how to use Phoenix's prompt playground
Last updated
Was this helpful?
To first get started, you will first Configure AI Providers. In the playground view, create a valid prompt for the LLM and click Run on the top right (or the mod + enter)
If successful you should see the LLM output stream out in the Output section of the UI.
The prompt editor (typically on the left side of the screen) is where you define the prompt template. You select the template language (mustache or f-string) on the toolbar. Whenever you type a variable placeholder in the prompt (say {{question}} for mustache), the variable to fill will show up in the inputs section. Input variables must either be filled in by hand or can be filled in via a dataset (where each row has key / value pairs for the input).
Every prompt instance can be configured to use a specific LLM and set of invocation parameters. Click on the model configuration button at the top of the prompt editor and configure your LLM of choice. Click on the "save as default" option to make your configuration sticky across playground sessions.
The Prompt Playground offers the capability to compare multiple prompt variants directly within the playground. Simply click the + Compare button at the top of the first prompt to create duplicate instances. Each prompt variant manages its own independent template, model, and parameters. This allows you to quickly compare prompts (labeled A, B, C, and D in the UI) and run experiments to determine which prompt and model configuration is optimal for the given task.
Phoenix lets you run a prompt (or multiple prompts) on a dataset. Simply load a dataset containing the input variables you want to use in your prompt template. When you click Run, Phoenix will apply each configured prompt to every example in the dataset, invoking the LLM for all possible prompt-example combinations. The result of your playground runs will be tracked as an experiment under the loaded dataset (see Playground Traces)
All invocations of an LLM via the playground is recorded for analysis, annotations, evaluations, and dataset curation.
If you simply run an LLM in the playground using the free form inputs (e.g. not using a dataset), Your spans will be recorded in a project aptly titled "playground".
If however you run a prompt over dataset examples, the outputs and spans from your playground runs will be captured as an experiment. Each experiment will be named according to the prompt you ran the experiment over.
