  The generation of structured data in formats such as JSON, YAML and XML is a
critical task in Generative AI (GenAI) applications. These formats, while
widely used, contain many redundant constructs that lead to inflated token
usage. This inefficiency is particularly evident when employing large language
models (LLMs) like GPT-4, where generating extensive structured data incurs
increased latency and operational costs. We introduce a domain-specific
shorthand (DSS) format, underpinned by a context-free grammar (CFG), and
demonstrate its usage to reduce the number of tokens required for structured
data generation. The method involves creating a shorthand notation that
captures essential elements of the output schema with fewer tokens, ensuring it
can be unambiguously converted to and from its verbose form. It employs a CFG
to facilitate efficient shorthand generation by the LLM, and to create parsers
to translate the shorthand back into standard structured formats. The
application of our approach to data visualization with LLMs demonstrates a
significant (3x to 5x) reduction in generated tokens, leading to significantly
lower latency and cost. This paper outlines the development of the DSS and the
accompanying CFG, and the implications of this approach for GenAI applications,
presenting a scalable solution to the token inefficiency problem in structured
data generation.
