Transforming Enterprise Search with Generative AI’s Large Language Models(LLMs)

Did you know that, as per Gartner’s survey, “47% of digital workers struggle to find the information needed to effectively perform their jobs”. One of the reasons many digital workers or employees struggle to find relevant information is because traditional enterprise search solutions that many employees/digital workers use do not yield relevant results.

If you are a typical business user in a company, you will search for information in the company portal, internal websites, various applications, or the company’s document store to do any of your tasks at least a few times a week. Most of these searches may or may not yield relevant results all the time. Except for applications-based search, most of the results may look like standard Google or Bing search, where you may need to look again into the document or page to get your information.

Below is an example of a site search on USCIS website for "What is US Visa". Many of the traditional enterprise search solutions look like in the below screenshot.

Traditional enterprise search solutions used by employees in a company are very much based on legacy approaches that rely on indexing and retrieval based on keywords. Most of the solutions look at keywords, try to do keyword matching, and spit out a list of files or pages that have those keywords.

The biggest issue with traditional approaches to enterprise search is that most of the solutions do not understand the context of the words of the worth and how to get the precise answer the employee is looking for. With the advent of Generative AI’s Large Language Models(LLMs), this traditional search based on keywords changes.

How Large Language Models Transform Enterprise Search?

Generative AI’s Large Language Models are trained on Petabytes of data and are amazingly good at understanding language and various relationships between words. When a user asks a question on anything that it is trained on, they are really good at giving a precise response in a succinct way.

When you use an LLM-based solution for enterprise search, business users get the result based on the context in a precise way. Users do not have to try again to figure out where the answer is in a document or look through a bunch of links or pages. With LLMs based approach, the user would get a precise answer with references to how the answer was generated based on the relevant data.

The enterprise search powered by the LLMs-based approach will fundamentally transform how business users interact with data on a daily basis. This new approach with LLMs saves a lot of time as there is no need for the user to again to figure out the exact answer. Also, the quality of the answer is much higher as it is not keyword centric approach.

How to implement Enterprise Search with LLMs?

There are a few approaches to working with LLMs for enterprise search.

1. Train an LLM from Scratch: If you really want to start from scratch, you could literally train an LLM from scratch using tons of unstructured and structured data. This is not the preferred approach as it could cost a lot of money and requires a lot of data. In this approach, LLMs can hallucinate as the data is compressed within the LLM weights. Also, there is no direct references to the given answer as the data is distributed across the LLM weights.

2. Finetune an LLM Model: Another approach is to fine-tune a foundational LLM based on domain-specific data. You use this approach, if you do not have a lot of data and money. But, the problems from the first approach such as hallucination, lack of direct references still exist with this approach.

3. LLMs working with Vector Databases: The most common and typical approach is to work with LLMs in conjunction with vector databases. The advance with this approach is, the LLMs have access to the actual data and references to the same. Hence, the accuracy of the response is high and there is less chance of hallucination. Also, this is a cost-effective way to build an enterprise search solution. This approach is also called RAG(Retrieval Augmented Generation).

If you do not have the expertise to build enterprise search based on LLMs, you can use our A2O Corpus to build enterprise search solutions powered by LLMs. We offer the service as SaaS, so you, as a customer, do not need to worry about any of the LLM technical stuff and consume it right out of the box.