How Vector Embedding Search Can Be Used In the Enterprise

How Vector Embedding Search Can Be Used In the Enterprise
As opposed to simple keyword matching, vector embeddings provide a more effective way to search for information on the internet or ChatGPT application.

ChatGPT and generative AI continue to make a significant buzz in the business world, especially considering their ability to automate the process of content creation. This includes generating text-based documents ranging from poetry to programming code. However, these AI-based innovations also improve the ability for a bot to read and understand complex queries, potentially revolutionizing the lucrative practice of searching for information on the internet. 

Vector embeddings lie at the heart of the AI revolution in internet search. A vector embedding provides the means to mathematically identify each word and its underlying context. The difference between two words (combining its meaning and context) is known as semantic distance. For example, “cat” and “dog” are relatively close in semantic distance compared to “green pepper” and “pencil.” This innovative idea provides the secret sauce to turn internet search upon its head. 

Searching for information using vector embeddings also provides the hope for significant benefits to the enterprise. It offers the potential to quickly find relevant information related to the context implied in a search – a query using actual sentences and phrases as opposed to a keyword or two. Expect it to make searching for something on the venerable company intranet as obsolete as a rotary telephone. 

Vector Embeddings Brings a Mathematical Approach to Internet Search

As highlighted above, vector embeddings are a mathematical representation of a word and its context. The closeness between two ideas or concepts expressed in a word or phrase is known as semantic distance. For example, this approach allows simple math equations to help explain the underlying contextual definition of a word.

King – Man + Woman = Queen

Washington D.C. – USA + Canada = Ottawa 

Granted, these examples are simplified, but provide insight into how math allows both vector embeddings and semantic distance to be used to greatly optimize searching for information. Context remains a critical part of returning information relevant to what the user actually wants. This is especially the case when searching with words that have more than one meaning. 

These embeddings allow words to be represented in a three-dimensional space. This allows simple mathematical equations, like cosine similarity, to find matching documents by their semantic distance. So in the near future, when searching for reading glasses, expect not to see results that also include glassware used for drinking. 

As opposed to simple keyword matching, vector embeddings provide a more effective way to search for information on the internet or even any corporate knowledge base, intranet, or corporate ChatGPT application

Users receive a more meaningful collection of search results helping them find what they actually wanted. Let’s take a closer look at how this aspect of generative AI and large language models brings massive potential for companies in a variety of business sectors. 

Strategies for Businesses to Optimize Internet Search With Vector Embeddings 

Our simplified explanation on how vector embeddings optimize the traditional internet search hopefully provides a basic understanding of the concept. Now let’s dive into some practical examples of how to leverage this tech innovation at your own organization. After all, with ChatGPT generating so much buzz, vector embeddings sometimes get overlooked. The “red alert” issued at Google/Alphabet after ChatGPT and the layering of knowledge entered the public consciousness reveals its real potential to disrupt the tech behemoth’s main revenue stream.

Online retailers potentially benefit from vector embeddings and semantic distance to return better search results for existing customers. They analyze the customer purchase history along with their query string to return results that better match the context of the customer’s search. In fact, this approach also offers the potential to improve the search results for potential new customers. 

Instead of companies trying to game Google’s search engine with arcane SEO techniques that ultimately offer little benefit to users, they can use vector embeddings to provide customers – existing and new – a better online retail experience. Expect improved sales and brand loyalty as a result. 

Knowledge Sharing Becomes More Effective With Vector Embeddings 

As noted earlier, vector embeddings also provide the potential to greatly improve knowledge sharing at businesses. Consider a new employee trying to find out relevant information about the company’s product line. While searching for this information on the intranet is typically more effective compared to a full internet search using Google, vector embeddings offers that extra context to return a better result set.

This technology provide the means to greatly improve corporate training and the onboarding process for new employees. This is especially the case when self-training options are used as opposed to an instructor-led course. It empowers employees to get their questions answered, quickly and accurately. Remote workers also stay more engaged and inspired.   

If your business wants to leverage the promise of vector embeddings, connect with the experts at NineTwoThree. We boast a keen mix of ChatGPT app development skills, business acumen, and entrepreneurial spirit, making us a perfect partner for your company. Reach out to us to discuss the possibilities of a partnership.

Tim Ludy
Tim Ludy
Articles From Tim
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