Prime Legal AI is the AI solution for the legal industry. Save time and effort in your daily work and focus on what matters most. With PrimeLegal AI, you’ll never do the same task twice, eliminating tedious customizations and leveraging expertise from your own databases or hundreds of thousands of legal consultations applied to your…
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PLAI provides various Large Language Models that you can choose for your legal work. A Large Language Model or LLM is a computer program trained to understand and generate human-like texts. It can answer questions posed in natural language, compose texts, and be employed in various language-related tasks. It learns from vast amounts of human…
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For PLAI to act on your behalf and complete tasks, it needs your instructions. Your tasks or questions to the AI are called “Prompts” and are entered via the PLAI interface, similar to a chat. Here you can request legal tasks such as creating an employment contract or answering legal questions. There are certain pre-set…
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On what context should PLAI rely for your task? In other words, based on which information should the AI provide its response? You can create collections based on your company’s internal data, such as template contracts and all contracts, legal briefs, analyses, opinions, lawsuits, pleadings, etc., ever created. Choose one or more collections for each…
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All files you upload to PLAI – whether entire datasets for collections or individual documents as attachments – can be anonymized upon request. Anonymization, which involves redacting or obfuscating personally identifiable information, is an essential requirement for the secure and privacy-compliant use of your own datasets. You have the autonomy to decide which types of…
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If you stay within a session, you can incorporate the previous PLAI dialogue into your new prompts. This allows for the inclusion of previous questions and answers in the new prompt. For example: “Condense the text just created and phrase it so that even legal laypeople can understand.” You can ask follow-up questions to the…
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The temperature parameter influences the randomness of the LLM’s response. The outputs are not deterministic; instead, probabilities for various responses are calculated. The temperature, typically ranging from 0 to 1, regulates this probability distribution. A higher temperature value (close to 1) increases randomness, leading to more diverse and creative responses. A lower value (close to…
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PLAI utilizes a semantic search algorithm to provide relevant context for the LLM. Semantic search, also known as vector search, goes beyond traditional text-based search algorithms. Its aim is not only to find exact keyword matches but also to understand the meaning of words and the context of a query. Vector search employs vectors to…
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