What is an effective method to preprocess prompts using custom code before sending them to an LLM?

Question

Options

• ADirectly modify the LLM's internal architecture to include preprocessing steps
• BIt is better not to introduce custom code to preprocess prompts as the LLM has not been trained
• CRather than preprocessing prompts, it's more effective to postprocess the LLM outputs to align
• DWrite a MLflow PyFunc model that has a separate function to process the prompts

• A
  4% (1)
• B
  8% (2)
• C
  4% (1)
• D
  83% (20)

Explanation

MLflow PyFunc (option D) provides a flexible model wrapper that allows engineers to define a custom predict method containing arbitrary Python code - including prompt preprocessing logic - before the call is forwarded to an LLM. This is a standard production pattern for encapsulating preprocessing, validation, and transformation steps in a deployable, versioned artifact. Option A (modifying LLM internal architecture) is neither feasible nor appropriate for this use case. Option B is incorrect - custom preprocessing is a valid and common practice. Option C (postprocessing instead of preprocessing) addresses a different concern and doesn't solve upstream prompt preparation needs.

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