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Format consistency

As explained in Prompt Engineering in Practice

Format consistency is the practice of keeping corresponding examples and outputs in a uniform structural shape so that a model receives an unambiguous pattern.

Format consistency keeps few-shot examples structurally uniform so the model has a clear pattern to extend. Matching field names is not enough: corresponding values should also have the same shape. section 7.3.2

For a classification prompt:

  • Keep the input and output fields consistent across reference examples. section 7.3.2
  • Use the same shape for classification values; do not mix bare labels with labels followed by explanations. section 7.3.2

Code review classification

In an inconsistent prompt, one reference ends with “Classification: BugRisk” while another gives “Classification: Performance” followed by explanatory prose. In a consistent version, both references provide only the classification label. The target asks for a classification in that same format. section 7.3.2

Test the effect by building paired prompts for the same engineering classification task: one with mixed formatting and one with repeated formatting. Compare their output structures. section 7.3.5

How Prompt Engineering in Practice covers it

How deep it goes
Contrasts consistent and inconsistent example shapes, including bare labels versus labels with explanatory prose.
Examples
Uses code review classification and proposes stress tests with pull-request triage or incident severity prompts.
What it assumes
Builds on prompt structure and few-shot design rather than introducing prompt construction from zero.
What you can do after
Apply it by creating paired few-shot prompts with mixed and repeated formatting for an engineering classification task.
How it connects
Links few-shot prompting with in-context learning, prompt structure, and classification.
Cover of Prompt Engineering in Practice
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