1. Accuracy: Before your researchers commit to additional resources, it is imperative that the application provides accurate answers to your questions. Particularly in the fields of life sciences, medicine, and clinical domains, reliance on such a system is only possible if it provides accurate and current information. While certain limitations of Large Language Models (LLMs), such as hallucinations and bias, may lead to inaccuracies, can your application effectively address these challenges?
2. Provenance: Even if the summarized answer is accurate, it may still be insufficient. Can you justify conducting research solely based on the fact that “the LLM said that”? The application should enable you to trace the answers back to the reference documents, whether they are external or internal. Evidence-based decision-making is paramount in life sciences, where wrong decisions can have dire consequences.
3. Transparency: Let’s push for more. Why settle for just a list of reference documents? The application should provide insights into why it considers the given reference documents relevant to the search and highlight which sections of the documents contain evidence that contributed to the answer.
4. Domain expertise: The data within the Life Sciences field can be intricate and rife with ambiguities. It’s imperative that the application can navigate this complexity without becoming entangled in subtle nuances, terminologies, abbreviations, and similar intricacies.
5. Dynamic source selection: Your question might pertain to a competitor, internal research, a patent, or a clinical trial – relying on a single document source may not yield all the answers you seek. To cater to a broader range of users, an application must support a diverse array of data sources and possess the capability to dynamically switch between them based on the nature of the questions being posed.
6. Security & privacy: Regardless of whether it concerns the confidentiality of the questions posed or the user’s access level to the document housing the answer, the application must uphold data privacy and respect the user’s access permissions.
7. Operational efficiency: It’s a technology currently in high demand, and one doesn’t require expertise in economics to grasp the substantial operational costs associated with the utilization of generative AI and LLMs, along with their accompanying computing expenses. To ensure a system remains up to date, the application must strike a delicate balance, meeting most of the requirements without depleting all available funds.

As an advantage, the application renders the answer-generation process entirely transparent. It utilizes ontologies to provide clarity on how and why results were identified. It maintains not only a list of relevant documents but also segments of the documents utilized for answer generation, along with an explanation of why it considers them to contain the answers. This stands in contrast to any other system that operates as a black box and lacks the ability to offer this level of transparency.