Large Language Models - The Future of Enterprise Knowledge Search

Reducing the time to knowledge search is everything when it comes to increasing workers’ productivity and generating business value. Employees want data every now and then to help them get going, be motivated, and be involved in their core processes.

By 2025, smart workflows and digital interactions will be standards for enterprises, which will further drive towards a data-driven enterprise, predicted McKinsey. But workplace data isn’t easy to find for employees.

With enterprises already overbrimming with digital tools spread across different locations and departments, employees need data as a way to optimize every aspect of their tasks.

The barrier to workers’ productivity and business growth is nothing but an enterprise knowledge search that is disparate, complex, and traditional, restricting employees from locating key information across an entire enterprise knowledge ecosystem.

LLMs or Large Language Models that power Generative AI can prove a game changer in augmenting enterprise knowledge search experience for employees by enabling them to derive accurate responses from every NLP-based search query without losing context to the information and time.

Let’s know how you can transform knowledge search experience across the enterprise ecosystem with LLMs and what the future holds for this superpower AI technology.

Every enterprise has a unique requirement for knowledge search, which is not similar to what other enterprises seek in their search systems.

A search method that works for every Google user may not work for employees in an enterprise setting as they require specificity in internal search queries and no repeated and vague information. As a result, the lack of context needs developers and IT workers to spend almost 4.2 hours finding relevant answers, as discovered by the Workplace Relevance Report 2022.

The following issues make enterprise knowledge search hard:

The complexity of enterprise knowledge

• Information is spread across multiple search systems, such as the ITSM platform, CRM, ERP, HR Portal, etc

• Systems are not synced to provide a single pane of glass view

• Each system has varied levels of query languages and ranking algorithms, dissimilar to each other

• A similar search query does not surface appropriate results

• Multiple versions of the same document lead to confusion

• Users need a steep learning curve to work with the systems

• The traditional system is flexible with structured data

• Unstructured data is not modeled properly

• Inappropriate metadata tagging prevents data from being pulled up through links in social media, or collaboration channels

• Wrong metadata may surface search results from one data point for a wide range of queries, denying the desired result

Time a major constraint for knowledge management

Based on their experience with ticket handling and resolution, enterprise leaders must consider creating new resources to share with the team and provide a way to resolve issues at scale for unique cases. But, pulling information from the systems is a time constraint for knowledge workers, delaying the drafting and approval of the information.

Outdated and contextless internal knowledge

Enterprise applications or software are subject to revisions for process efficiency, which need continuous updating of internal knowledge resources for end users, such as employees and customers. But, the constraint is to get subject matter experts or technical writers to work on updating the resources. The internal knowledge database accommodates the information that does not support knowledge relevance.

Ineffective semantic search capability

Most knowledge search systems use keyword-matching retrieval methods, not semantic search capability. As a result, a model only synthesizes keywords and does not parse a search query to surface what is desired rather than repeated information.

End-user adoption is not as expected as desired

Lots of knowledge search systems miss out on conversational capabilities. Although the enterprise’s eagerness to reduce human-assisted support through digital workflows is at its peak, the system lacks intuitiveness. Users are less likely to use self-search functionality and rather more engaged in connecting with human assists.

Large Language Models, however, hold promises to overcome these existing enterprise problems associated with knowledge search and management. There are many ways an enterprise can try to amplify knowledge search experience using LLMs and generative AI while helping you reduce certain types of shortcomings in the model. (We will discuss this later)

Large Language Models are deep learning-based models to parse NLP or natural language queries to produce human-like responses.

Large language models are trained on large data resources such as ebooks, books, social media posts, and the entire internet. The more data it has, the better at searching sources with accurate prompts or what it is asked for.

In addition to LLM training, this model also uses an unsupervised learning approach to be trained, which means it needs zero-shot or minimal to zero supervision to be fed with algorithms or parameters.

With massive datasets processing inside it, LLMs make it easy to detect the next sequence of a search query or a phrase, thus, improving search response to be human-like, interactive, and intuitive.

That’s a reason why enterprises can benefit from the LLMs' properties in improving knowledge search internally and providing improved responses by connecting to conversational AI technologies.

A transformer model built with a deep learning network is at the core of a large language model. It unleashes enormous capacity to encode, decode, or process large language inputs using its huge data model being trained on large datasets.

The transformer follows several key steps to generate or process an input or a prompt:

• Receives text inputs or a sequence of words or large texts

• Transforms these text inputs into individual tokens

• Encodes tokens into context vectors or embeddings, or mathematical representations

Once the vector representations are done, the decoder inside the transformer generates the desired output.

For example, an LLM model detects the intent, searches across its huge database, and verifies the context of a prompt. Since it can process large language texts, it can well analyze and produce what is asked for, regardless of whether a prompt has different meanings. The output it produces is accurate and contextual.

It is no secret that LLMs are trained on unsupervised learning, which keeps them open for inaccuracy and hallucination and contributes to Blackbox challenges.

One great way to mitigate the chances of inaccuracy and vague ideas is to use conversational AI to connect LLMs to internal or external knowledge bases. Since conversational AI is built on supervised learning and is subject to continuous monitoring, an LLM transformer will be able to generate valid and true responses specific to the enterprise context and encourage valid enterprise use cases.

For example, the power of conversational AI enables the LLMs to generate responses along with the sources linked to the responses from the internal or external database, therefore establishing the veracity of the suggestion and improving wide user acceptance without wasting time and productivity.

In addition, Workativ harnesses the best of LLM algorithms on top of its conversational AI chatbot builder to improve search relevance and accuracy.

To improve the performance of LLMs, the best way is to train the models with company-wide knowledge resources. Also, external databases can be twice as useful for workplace support automation.

In order to leverage the properties of LLMs to improve enterprise knowledge search, API calls or backend integrations are effective. This is a fast and simple process to build a hybrid model that easily connects your enterprise knowledge and external databases to LLMs and utilizes conversational AI to augment search relevance and retrieve data useful for performing workplace tasks.

Knowledge search augmentation

LLMs or generative AI are conveniently adaptive to enterprise work assistants. For instance, if you have a virtual assistant for IT or HR platforms to alleviate user effort, you can leverage Gen AI to produce more natural conversations and provide resources that would otherwise seem overwhelming and exhaustive.

Say, a new hire needs enterprise resources to adapt to the company culture and various process policies. With conversational AI underpinning contextual awareness, LLMs make knowledge search feel more intuitive, like that of searching on the web and help find the exact knowledge rapidly.

Increase in user productivity

Integrating LLMs with conversational AI helps accelerate and automate relevant content suggestions.

As LLMs use semantic search capability, it reduces the time to crawl and index metadata spread across enterprise knowledge data sources. As a result, anything, be it a link or folder, can be retrieved easily, improving users' search experience and enabling them to work with the desired information they need.

Automation of repetitive tasks

Leaders can use generative AI to automate repetitive tasks in the enterprise setting by streamlining workflows. By integrating enterprise knowledge resources, leaders can improve search relevance for their users, which further improves auto-resolution capability.

Say, your user needs assistance resetting passwords for an application that remained expired for a long time. After retrieval of the service, it requires login credentials and a password.

With automated workflows built with LLMs at their core, users can continuously improve their search method and retrieve the knowledge that is specific to password reset issues for that particular application and not for other applications which may seem similar.

To combat LLMs' shortcomings, conversational AI provides a competitive advantage to ensure the veracity and verifiability of the produced knowledge search by LLMs.

At one end, internal users can generate knowledge that is tuned with LLM architecture, meaning the common type of information such as media content and various related answers, and on the other end, more specific answers to their knowledge queries that help them solve enterprise issues.

However, since outputs generated by Large Language Models are not accurate in certain scenarios, considering the training methods they encompass, fine-tuning and prompt engineering make it easy to leverage the model benefit.

Let’s also be mindful of high development and deployment costs, including a long time to market.

In such a scenario, Workativ virtual assistant or our conversational AI platform gives a simple and fast way to harness the properties of LLM while improving knowledge search for enterprise use cases.

For example, Workativ virtual assistant can easily integrate with enterprise applications such as ITSM platforms or HR tools to improve workplace support. By leveraging LLMs for its chatbots, we aim to improve enterprise knowledge search, enhance user productivity, and mitigate downtime to drive business outcomes as much as possible. Connect with us to learn more about LLMs in the enterprise setting.

Enterprise knowledge search, which is rigid and inappropriate in the current scenario of remote and hybrid work settings, can be augmented using LLMs and conversational AI.

The traditional knowledge search method can go through a transformation and look promising with the advancement of LLMs in the coming years. The shortcoming they currently possess can be eliminated with continuous monitoring of LLMs' activity, and by establishing the verifiability of knowledge they are trained on. On top of it, conversational AI makes LLMs more intuitive for enterprise users for their day-to-day activities as they rely on effective knowledge search results to perform their tasks.

In a nutshell, it all takes to ensure that LLMs are connected to reliable knowledge sources so that they can enhance the user experience through augmented retrieval of accurate information with minimal effort.

Are you interested in learning more about LLMs and conversational AI capabilities to design enterprise-specific use cases and augment workplace support?

Book a demo with Workativ today.