Generative AI is transforming various sectors, with numerous organizations aiming to incorporate this innovation in fields such as customer service, market intelligence and software development. In fact, IDC reports that 55 percent of financial and telecom organisations will invest in generative AI throughout 2023. Furthermore, Gartner predicts that by 2026, 80 percent of organisations will have deployed generative AI-powered application programming interfaces (APIs) and models.
Clearly, the future of generative AI is bright, with technologies like ChatGPT leading the way. However, to truly weave this potential into the fabric of employee operations, organisations must go beyond off-the-shelf solutions that rely solely on publicly available data.
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The real power unfolds when AI can understand and interact with proprietary data unique to each organisation, from product catalogs to customer histories. For this, the right infrastructure, centered around the use of vectors to represent and interpret this complex data, is critical. This is where vectors come into play, ensuring that generative AI's full potential is not just a question of when but also how effectively it can be tailored to each organization's specific needs.
The answer lies in vectors
Generative AI is key to unlocking future success opportunities for businesses. However, it is the large language models (LLMs) that help businesses gain answers or insights. It should be noted that LLMs do not function in the same manner as human brains do—they do not retain information from previous interactions. Rather, they leverage huge sets of questions and answers to provide users with detailed and specialised information.
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By converting the questions and answers into numerical data sets called vectors, generative AI can respond to user queries effectively. These sets contain word definitions and usage patterns that enable the technology to give near-correct answers in a human-like manner.
Comparing vector search to keyword-based search
Information that is represented as vectors allows for faster searches of large datasets comprising multiple formats through the use of "vector similarity search" (also known as "nearest neighbour search"). This feature determines how closely related queries are to the data sets, allowing users to discover results that are similar to what they are looking for, such as synonyms for certain words.
For example, inputting "happy" in a vector search will not only provide results that contain the exact word but also related terms like "cheerful" and "joyful". In a business context, vector search can enable applications to analyse customer profiles, images, and incidents so they can recommend relevant products or identify fraudulent transactions.
These functions differ greatly from traditional keyword-based search, which depends on specific terms to get the right information. Going back to the previous example, users searching for "happy" can only find articles and documents containing that exact word.
Authored by Mukundha Madhavan - APAC Tech Lead, DataStax
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