Why field order may not improve model reasoning

Field ordering in Pydantic schemas represents a subtle but potentially significant design choice for AI developers working with structured outputs. A recent experiment tests whether placing reasoning fields before answer fields in model schemas can nudge language models toward better performance, particularly in non-reasoning tasks where encouraging chain-of-thought processing might improve outcomes.

The experiment setup: The author used pydantic-evals to test whether field ordering impacts AI model performance.

• The study compared two schema configurations: “answer first, reasoning second” versus “reasoning first, answer second” across various GPT models.
• Testing used the painting style classification dataset from HuggingFace, creating both simple classification tasks and more complex tasks requiring multi-step reasoning.

Key results: The experiment found minimal performance differences between the two field ordering approaches.

• Data tables in the article show negligible variations in accuracy between the “Answer First” and “Answer Second” configurations.
• This pattern held consistent across different GPT model versions and across both easy and hard classification tasks.

Why this matters: Field ordering represents one of many subtle implementation choices developers make when designing AI applications with structured outputs.

• The hypothesis that placing reasoning fields first might improve model performance by encouraging chain-of-thought processing wasn’t clearly supported by the data.
• These findings suggest that other factors may have more significant impacts on structured output quality than field ordering alone.

The technical context: The experiment leveraged several modern AI development tools.

• The author utilized the recently released pydantic-evals framework specifically designed for LLM evaluations.
• Pydantic, a popular data validation library for Python, is increasingly used to implement structured outputs in AI applications.

The big picture: While this specific experiment didn’t reveal dramatic effects from field ordering, it highlights the ongoing exploration of subtle factors that might influence model behavior.

• As developers continue building AI systems with structured outputs, understanding these nuances becomes increasingly valuable.
• The author acknowledges the challenges in definitively explaining LLM behaviors, suggesting more research may be needed in this area.