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Overview

PromptL enables you to craft a controlled interaction history so that the model can be conditioned on arbitrary prior outputs. In advanced usage, there are two principle mechanisms of interest:
  1. Mocking Roles
    Pretend that the assistant has already replied with specified content.
  2. Mocking Tool Calls
    Emulate a tool invocation and its response so that PromptL sees it as if a real tool were executed.

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1. Mocking Roles

Mocking a role means inserting an assistant response directly into the prompt. The model will behave as though this response actually occurred. This is useful when you want to:
  • Seed the conversation with example assistant behavior
  • Use few-shot prompting techniques
  • Test how the model continues from a predetermined reply

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Syntax

Wrap the desired assistant reply between <assistant> tags: 
<user>
Good morning!
</user>

<assistant>
Hello, I’m your weather assistant. How can I help you today?
</assistant>
Some models do not allow assistant messages as the last item.

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2. Mocking Tool Calls

Mocking a tool call allows you to simulate both the invocation of an external function and its response—as if the model had called a real tool and received structured output. This gives you fine-grained control over how the model interprets prior tool usage and is especially useful in logic-heavy prompt flows that rely on external data.

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When to Use

  • Testing prompt branches that depend on tool results
  • Simulating APIs without triggering real network requests
  • Validating error-handling logic by crafting edge-case tool responses

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Syntax

A mocked tool call consists of two components:
  1. A <tool-call /> element inside an <assistant> block, which mimics the model making a function call.
  2. A corresponding <tool> block with the same id and name, which contains the tool’s response.
<assistant>
  <tool-call id="123" name="get-weather" arguments={{ { location: "Barcelona" } }} />
</assistant>

<tool id="123" name="get_weather">
  It's 17 °C in Barcelona.
</tool>
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