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Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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Enhancing structured data generation with GPT-4o evaluating prompt efficiency across prompt styles.

Ashraf Elnashar1, Jules White1, Douglas C Schmidt2

  • 1Department of Computer Science, Vanderbilt University, Nashville, TN, United States.

Frontiers in Artificial Intelligence
|April 10, 2025
PubMed
Summary

Prompt style significantly impacts large language model (LLM) performance for structured data generation. JSON, YAML, and Hybrid CSV/Prefix prompt styles offer different trade-offs in accuracy, token cost, and processing time for applications like medical records.

Keywords:
GPT-4oHybrid CSV/PrefixJSONYAMLcost-effective AIprompt engineeringstructured data generationtoken efficiency

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Area of Science:

  • Artificial Intelligence
  • Natural Language Processing
  • Data Science

Background:

  • Large language models (LLMs) offer advanced capabilities for structured data generation.
  • Prompt engineering is crucial for optimizing LLM output accuracy, efficiency, and cost.
  • Evaluating different prompt styles is essential for practical LLM applications.

Purpose of the Study:

  • To compare the effectiveness of JSON, YAML, and Hybrid CSV/Prefix prompt styles for structured data generation using GPT-4o.
  • To analyze the impact of prompt styles on data accuracy, token cost, and processing time.
  • To provide recommendations for selecting optimal prompt styles based on application requirements.

Main Methods:

  • Utilized randomized datasets for personal stories, receipts, and medical records.
  • Evaluated three prompt styles: JSON, YAML, and Hybrid CSV/Prefix.
  • Measured performance across accuracy, token cost, and processing time with structured validation.

Main Results:

  • JSON excels in accuracy for complex data structures.
  • YAML provides a balance of readability and efficiency.
  • Hybrid CSV/Prefix demonstrates superior token and time efficiency for flat data.

Conclusions:

  • Prompt style selection involves trade-offs between complexity and performance.
  • Tailored prompt strategies are key for optimizing LLM-driven data generation.
  • Further research is needed to enhance LLM capabilities for complex structured data tasks.