The technique of prompt chaining, with its multi-step approach, can significantly boost the performance of Large Language Models in classifying elaborate legal documents. The research detailed in Large Language Model Prompt Chaining for Long Legal Document Classification incorporates a systematic methodology:

• Summary Creation: Summarizing extensive documents as a preparatory step.
• Semantic Search: Finding related texts and annotations to inform the prompt.
• Label Prompting: Leveraging in-context learning for accurate classification.

The results reveal that prompt chaining not only outperforms zero-shot methods but also leads to better scores than more extensive models. The paper demonstrates an inventive application of LLMs in legal tech, potentially transforming the industry with more nuanced document analysis. It underscores the value of strategic prompts in maximizing LLMs’ understanding of specialized content.