The article discusses a framework employing large language models (LLMs) for taxonomy generation and text classification. It emphasizes the importance of designing prompts that specify output formats, such as markdown tables for clarity. Additionally, the framework incorporates guardrail tests for verifying output adherence to these formats and maintaining robustness throughout the prompt chain. Such strategies aim to improve the consistency of classifications while minimizing the need for extensive post-processing adjustments, ensuring an efficient and executable system.
To ensure our LLM-based framework is robust, we design guardrail tests that verify if the output adheres to specified formats for parsing.
Our approach includes explicitly stating output formats in prompts and structuring outputs as markdown tables to enhance consistency and reduce post-processing effort.
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