The difference between generative AI and extractive AI is straightforward. Yet, Lazarus AI still sees these approaches confused on a daily basis. This confusion prevents companies from getting real value out of AI and creates misconceptions about the viability of enterprise AI solutions. Both approaches are powerful and both have uses in the modern corporation. Misusing one of these approaches is like misusing any other tool: it can lead to endless frustrations and inefficiencies.
Read moreThe decision between a point solution and a platform depends on your organization’s specific needs, budget constraints, and long-term vision. Assess the trade-offs in terms of functionality, ease of implementation, scalability, and integration capabilities to make the most appropriate choice for your document understanding needs.
Read moreThis insight presents a perspective on the concept of Explainability in AI. This topic will be of intense interest while both State and Federal authorities work through the rules of the road. In the interim, insurers need to strive for explainability and hold their partners accountable.
Read moreThis Insight presents five steps needed for success in the world of prompting. As noted earlier, prompting is both art and science and prompting will continue to evolve quickly. Following the Steps and guidance here will maximize probability of success and ignoring the guidance here will increase risk, money, and time. Continue to watch Lazarus for Insights as this evolution occurs.
Read moreEffective prompting is a vital component of implementing LLM solutions in the insurance industry. To keep up with the current state of AI technology, insurers should look to develop their prompt engineering capabilities in 2024. Knowledge workers across all domains will need to learn prompting skills to effectively use LLM-based tools (general prompting). Dedicated prompt engineering professionals will not disappear, rather their responsibilities will shift towards large-scale and specialized prompting tasks (enterprise prompting).
Read moreThis Insight leverages Lazarus AI’s experience in the insurance industry to present a simple framework for conducting an effective POC. Many insurers have successfully completed a POC and implemented AI technology in production. In the coming year, many more will. We at Lazarus AI are available to help you whether you are just starting to develop use cases or are ready to dive into a POC of your own.
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Our cutting-edge technology ensures an infinite token context window, allowing our models to comprehend vast amounts of information with unparalleled depth and accuracy. With this capability, our AI systems can process data across diverse modalities and timeframes, empowering users with comprehensive insights and actionable intelligence.
We build proprietary LLMs and VITs for information understanding, few-shot classification; and document segmentation, summarization, and extraction. We utilize both decoder only and encoder-decoder type transformers that range in size from 7 to 256 billion parameters.
A better way to store and search your data, VKGs are temporality enabled relational databases that exist in a multi-dimensional hyperspace and can allow for: semantic search, the addition of new information, and the creation of search citations to pinpoint relevant information.