Prompt Playground

The prompt playground enables you to design, test, and refine prompts by selecting language models, configuring connections, and defining message types. Here’s a step-by-step guide to navigating the playground effectively.

Select LLM and choose connection and model

Write and configure message prompts

Test prompts output

Step 1: Select the LLM

• Obligatory: The first step is to select the LLM from the dropdown. Choose the appropriate LLM that suits your prompt requirements.

Step 2: Create or Choose a Connection

• After selecting the LLM, you must establish a connection. You can either create a new connection or choose from existing ones:

  • Click + New connection if a new setup is needed.

  • Alternatively, select from the predefined connections if available.

Step 3: Specify or Choose the Model

• In the Model field, either type the name of the model directly or use auto-selection from suggested options.

• This step ensures that your prompt is routed to the correct model associated with the chosen LLM and connection.

Step 4: Writing the Prompt

• In the Prompt section, you can define the user input, responses, and instructions:

  • Type your prompt text in the Enter text here field.

  • Prompts can include dynamic variables written as {{variable_name}}. During runtime, these placeholders will be populated with specific values. For example, {{user_name}} will be replaced by the user’s name if that variable is defined.

Step 5: Configure Message Types

• By default, messages are set as User messages. However, you can change the message type as follows:

  • Select System for messages intended to provide background information or instructions that influence the model’s behavior.

  • Choose Assistant to predefine responses as if they are coming from the assistant itself.

  • This flexibility allows you to simulate various conversation flows, ensuring your model responds correctly based on message type.

Step 6: Adjust Temperature and Max Tokens

• Temperature: Control the randomness of the model’s responses. A lower temperature (e.g., 0.3) makes responses more deterministic, while a higher value introduces variability.

• Max Tokens: Set the maximum number of tokens (words, punctuation, etc.) for the response. This limits the response length, ensuring outputs are concise when necessary.

Step 7: Using Streamed Responses

• Stream: Toggle the Stream switch on or off based on preference:

  • On: Responses will be streamed, appearing in real time as they are generated by the model.

  • Off: The entire response will appear at once after generation.