While AI solutions – and, more specifically, LLMs like GPT-4 or BERT – are often celebrated for their potential to transform businesses, one of the greatest breakthroughs of these models, arguably, is their intuitive ease of use. However, this also creates the misconception that LLM models are plug-and-play solutions that drive value on their own and can solve problems in isolation. That’s not the case.
There are many AI-driven solutions being touted as capable of solving enterprise-level challenges, but they fail to discuss the infrastructure and data requirements for success. This needs to brazil whatsapp number data change. LLMs can only deliver value when supported by robust infrastructure and high-quality, well-structured data. This is particularly true when thinking about leveraging LLMs to drive new levels of accuracy or personalization, such as tailoring content to match a brand’s tone of voice or specialized industry terminology. In addition, common enterprise use cases, like translating user-generated reviews, aren’t possible with LLMs on their own. Such tasks require an infrastructure that can handle large volumes of incoming content and turn around translations in near-real-time.
All of this requires an infrastructure where data flows seamlessly. Even more crucially, it demands sophisticated data repositories, such as translation memories, that are up to date and clean to fuel AI applications. Common approaches rely heavily on in-house, domain-specific data. The data feeding these AI systems must not only be of high quality but also properly formatted and organized to enable effective use and outputs.