Try the Prompt Playground on Literal AI

Create your first Prompt on the Playground and run experiments on multiple LLMs.

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Overview

You can access the playground via:

  1. The main menu
    Directly access a fresh playground to create, test and save prompts from scratch.
  2. Prompts
    Start from an existing prompt template: edit and run experiments to validate your template/settings changes.
  3. LLM Generations
    Investigate Generations from a real chat conversation and reproduce production issues.

If you start from scratch, you can open example templates, and iterate from there.

Depicted below is a view of the Playground opened on a Prompt.

The selected prompt and version show in the upper left corner.
Right of it, three actions:

The center pieces labeled 1 and 2 are respectively the Prompt Template and the Interactive Chat. We describe their contents in the following sections.

Playground Overview

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Prompt Template

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Template and Variables

Let’s take a closer look at the Prompt Template section:

The Template and Variables parts (1 & 2 below) really contain that new “programming language” to instruct LLMs. You can add multiple messages to your prompt:

  • System: Instructions about the role of the assistant. For example, you can say that the assistant should provide concise answers.
  • User: The user’s input, often enriched with context.
  • Assistant: An LLM response, to simulate a conversation flow.
  • Tool: Response to a tool call.

Prompt Template

Avoid exposing variables on System messages and letting user input reach. Prefer to use variables in User and Assistant messages.

User messages typically contain variables to embed a user query along with context. The example above shows a user message with two variables:

  • chunks: The chunks retrieved, say from a vector database.
  • question: The user’s query.

You can specify default values for variables but those are usually defined in your application code.

Variables follow the Mustache templating format.

Double curly brackets (mustaches 😉) always surround variable names.
A variable is written like {{variable}}.

If-statements and for-loops are written as {{#x}} ... {{/x}}:

  • if x is a boolean value, the section tags act like a conditional if statement
  • when x is an array, they behave like a for loop: access each element with {{.}}

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Tools

You can also declare tools on your prompt: given a tool description in proper JSON format, LLM models can determine whether a call to that tool is the next best action based on the user’s input.

If you need to specify the return value of a tool, use template messages above and select the Tool message type.

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Output types

Choose the output format for your prompt:

Output formats

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Text

text: free-text response

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JSON mode

json_object: JSON response with no specific schema enforced. Some providers ask to explicitly instruct the LLM to output JSON.

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JSON schema

json_schema: JSON response following the given JSON schema.

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Score schema

score_schema: specific to Literal AI and particular useful for LLM-as-a-Judge prompts. You can choose a Score Schema which forces your LLM to output a JSON of the form:

{
  "reason": "...", // The reason behind the chosen category
  "category": "..." // Category in score schema picked by LLM
}

Be advised that the format of json_schema itself varies from provider to provider.
Make sure to follow your provider’s specification.

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LLM Interaction

Interaction with LLMs takes place in the central panel:

LLM Interaction

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LLM Settings

Atop, you will find the LLM settings. Select any configured LLM provider and pick a model.
The settings icon lets you choose temperature, stop sequences, etc.

Provider & Model

Provider & Model

LLM settings

LLM Settings

Here’s a recap on classic LLM settings:

Temperature: Controls randomness. Lower temperatures result in less random generations (0 = deterministic). The higher the temperature, the closer to uniform token sampling.

Maximum Length: Maximum number of tokens to generate. Limits vary from model to model: anywhere between 1024 and 32,192.

Stop Sequences: Use up to four sequences where the API will stop generating further tokens. The returned text will not contain the stop sequence.

Top P or Nucleas Sampling: Controls diversity by restricting tokens to consider. Higher Top P values consider more possible tokens, even the less likely ones, which makes the generated text more diverse.

Frequency Penalty: Penalizes new tokens based on their existing frequency in the text so far. Decreases the model’s likelihood to repeat the same line verbatim.

Presence Penalty: Penalizes new tokens based on whether they appear in the text so far. Increases the model’s likelihood to talk about new topics.

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Try out & in-context debugging

The bottom box lets you input a multimodal message (text, image, etc.) to send to the LLM. You may also add messages from within the canvas to simulate a conversation flow / test a new idea.

If you accessed the playground via an LLM call, you get the full conversation context.
A must to troubleshoot production issues!

Use Cmd+Enter to send your message.

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Multiple LLMs

Finally, leverage multiple tabs to simultaneously run your prompt against different LLMs / settings. Selectively run tabs with the play button on the top right of each tab.

Multiple tabs

Once satisfied with your “vibes check”, it’s time to run experiments on larger datasets.

Saving, A/B testing and deploying your prompt comes in last.
Check out the Prompts documentation.

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