Persona-Based Legal Document Summarization with AI21 Studio

AI21 Studio proposes a persona-based summarization approach to make long legal documents easier to read. Legal professionals want summaries that preserve the original's legal terminology and format, while general readers want to quickly grasp the same content in simpler language.

The key lies in prompt engineering and few-shot prompt design. The article uses real documents like IRS legal letters as examples, repeating a Letter: and Summary: structure so the model learns the desired tone and format. More representative data is better, but it explains that LLMs can generalize fairly well even with just a few examples.

• The prompt for legal professionals preserves formal terms as much as possible to retain official expressions.
• The prompt for general readers adds more examples and compresses the content into key points using simple language.
• For generation quality, a temperature of 0.0~0.3 range is suitable, and examples and output are separated using a stop sequence like \n##\n.

The code example is designed to concatenate letters and summaries to construct make_few_shot_prompt, reliably generating different summaries for the same document depending on the target audience.