Prompt Decorator

Overview

The Prompt decorator automatically instruments LLM provider calls and creates Prompt Logs on Humanloop. When applied to a function, it:

• Creates a new Log for each LLM provider call made within the decorated function.
• Versions the Prompt using hyperparameters of the provider call.

Decorator Definition

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@hl_client.prompt(
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    # Required: path on Humanloop workspace for the Prompt
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    path: str
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)
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def function(*args, **kwargs): ...

Parameters

Usage

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@hl_client.prompt(path="MyFeature/Process")
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def process_input(text: str) -> str:
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    return openai.chat.completions.create(
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        model="gpt-4o-mini",
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        messages=[{"role": "user", "content": text}]
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    ).choices[0].message.content

Behavior

Versioning

The hyperparameters of the LLM provider call are used to version the Prompt.

If the configuration changes, new Logs will be created under the new version of the the same Prompt.

The following parameters are considered for versioning the Prompt:

Log Creation

Each LLM provider call within the decorated function creates a Log with the following fields set:

Error Handling

Best Practices

1. Multiple Logs will be created if you make multiple calls inside the decorated function. To avoid confusion, avoid calls with different providers or hyperparameters, as this will create multiple versions of the Prompt.
2. Calling prompts.log() or prompts.call() inside the decorated function works normally, with no interaction with the decorator. However, it indicates a misuse of the decorator, as they are alternatives for achieving the same result.
3. If you want to switch between providers with ease, use prompts.call() with a provider parameter instead of the decorator.

Related Documentation

Humanloop Prompts are more than the string passed to the LLM provider. They encapsulate LLM hyperparameters, associations to available tools, and can be templated. For more details, refer to our Prompts explanation.