ChatGPT is extremely powerful when it comes to increasing employee support productivity through self-service.

If we look into its fundamental capabilities, ChatGPT can help beef up self-service for employee support by

• Generating answers for common queries from the world of knowledge

• Crafting responses using NLP prompts

• Simplifying the understanding of critical support queries

• Creating data analytics reports

The only setback is the outcome, which is quite generic if you work with the public version of ChatGPT.

However, you can easily get the benefits that of ChatGPT while enjoying more personalized capacity and security by leveraging a Private ChatGPT.

With Private ChatGPT, you can easily elevate the typical self-service capacity for employee support and empower your employees to work autonomously.

You already learned about ChatGPT—but Private ChatGPT may seem new to you.

We’ll learn in this article,

• What is a private ChatGPT?

• How can a private ChatGPT help boost self-service for employee support?

Keep reading, and we’ll explore more sides of a private ChatGPT for self-service.

A private ChatGPT is a GPT-based chatbot or chat interface that exhibits ChatGPT-like qualities to provide personal or tailor-made solutions to users unavailable with public solutions.

More about a private ChatGPT

• Private ChatGPT leverages LLMs trained on internal knowledge repositories or corpora of datasets.

• It is designed with critical security features.

• It can generate domain-specific answers and solve problems at scale, including general answers.

Check the image below for an example:

When asked about the salary breakup, a private ChatGPT can give you straightforward answers.

It is because a private ChatGPT has access to internal HR systems and other company-wide systems that help retrieve specific answers.

On the other hand, if you ask the public version of ChatGPT, which has cut-off knowledge of up to September 2021, you can only get results similar to those of a search engine.

It means you need to provide all the critical data—a manual effort that is time-consuming and irritating.

Usually, your employees can call your HR to get full details.

This is where a private ChatGPT comes in and provides tailor-made solutions to reduce manual dependency and increase self-service capability, empowering your employees.

Self-service is an autonomous capability for customer or employee support. It allows users or employees to access knowledge bases or predefined FAQs to seek answers to their specific questions and solve problems independently.

The key objective of the self-service chatbot for employee support is to —

• Enable knowledge discovery

• Free agents of repetitive tasks

• Solve common problems at scale

• Reduce MTTR

• Reduce agent dependency

• Improve ROI

• Drive cost efficiency

However, self-service built with predefined knowledge articles or FAQs can often fall short of employee support expectations. As a result, the shift-left strategy may take a big hit. This would mean agent involvement would rise, and tickets would remain in the queue only to impact user experiences.

In contrast, a private ChatGPT can empower your employees and boost collaboration and efficiency for self-service.

Turning your internal knowledge bases or articles into ChatGPT-like abilities with the power of LLM that helps you generate Generative AI responses for user queries and solve problems autonomously is a private ChatGPT for self-service.

With a private ChatGPT, you extend the automation of self-service and gain more capabilities than the public version.

A private ChatGPT is effective in several ways across self-service due to the following reasons —

• Unique responses for new ITSM scenarios

• Faster NLP processing to enable rapid knowledge discovery

• Domain-specific responses to address company-wide issues

• Intelligent fallback for agent handover

A private ChatGPT can be very helpful for any business looking to leverage personalized or tailor-made solutions.

The perspective is that if your user asks a question, your LLM must contain relevant document text to answer it.

With any public version of the GPT model or ChatGPT, you can only access the world of knowledge or the Internet for a certain period.

A generic answer may not suffice. Employees need relevant answers to address a specific challenge related to HR or IT.

Integrating your LLM models or ChatGPT with the RAG system easily allows you to gain the capability of a private ChatGPT.

What is RAG?

RAG is an approach that allows users to search on a third-party resource within a Generative AI environment yet ensures the relevancy, accuracy, and context of an answer.

Retrieval Augmented Generation, or RAG, gives LLM the additional power to retrieve relevant answers for a question input through a semantic search.

It helps provide personalized responses and improves self-service adoption and engagement for employee support.

How does RAG work to build a private ChatGPT?

RAG or Retrieval Augmented Generation can work in the following ways:

• RAG allows building a hybrid NLU system, making it easy for the Generative AI interface to augment LLM searches by pulling up information from existing LLM databases and third-party or external resources.

• RAG needs LLM to use vector databases to break down inputs into embeddings or numeric representations.

• LLM searches look for answers from the LLM databases.

• When not found, RAG helps with semantic search and retrieves custom responses from external sources for LLM.

When integrated with RAG or hybrid NLU, you can transform the public version of GPT or ChatGPT into a private ChatGPT to address unique self-service problems and reduce service desk challenges.

Unlike ChatGPT or over-the-shelf GPT models, a private ChatGPT is designed to answer every unique question and elevate agent performance through enhanced productivity and efficiency.

1. Contextual responses

Suppose you use a typical chatbot for common questions and answers. You can get answers based on keyword matching. This can create a negative user experience, for pre-defined answers can be repeated.

A private ChatGPT uses RAG to leverage semantic search. This helps find information based on user intent in the LLM articles and external databases, boosting the relevance of responses.

So, if your employee asks, “This is not the right laptop?”. A private ChatGPT fetches information from ERP systems and provides a satisfactory answer.

2. Consolidated responses

What happens with a regular chatbot is that it fetches an entire article only to increase your effort. You must read the entire article, find the answer, and solve a problem.

A private ChatGPT can access the proprietary data repository and inherent LLM summarization capability. It can give relevant answers to company-specific problems and reduce unnecessary efforts required to pull the right answer for a specific problem through the summarization capability.

If your employee seeks help fixing a printer problem with a particular company product, a private ChatGPT can instantly assist.

Your employee can get summarized tips, including an article that contains all critical information about fixing a printer of a particular model.

3. Personalized responses

Ask a chatbot or ChatGPT about personal questions. It rarely can give answers. It can repeat ‘rephrase your queries ‘ or give a generic answer.

However, a private GPT or ChatGPT can make it quite easy for your employees and increase their satisfaction by providing personalized responses.

As RAG provides LLM searches using third-party resources or hybrid NLU, every employee can get personalized answers.

For example, if you ask for benefits enrollment, it can give concise and personalized answers without providing an answer that requires further calculations.

4. AI suggestions

Service desk agents can have extended flexibility and automation to accelerate the pace of responses. With a typical chatbot, employee support can suffer, as employees need better English knowledge to craft a message. This can lead to confusion and a vague understanding of the solution.

A private ChatGPT can learn about internal ITSM interactions and activities, using content generation capability to provide accurate and relevant answers.

For example, suppose an employee gets an agent call regarding software installs. In that case, the agent can craft meaningful messages and help appropriately despite being new and having limited capacity to craft messages.

5. AI summary

Agents often craft a summary of an incident for a closed ticket. This task may appear challenging for anyone. They often prefer overlooking the responsibility, leading to a fiasco when needed to pull data and create a report.

It is easy for agents to craft a summary as a private ChatGPT, enabling them to use internal ITSM data for actions to mitigate a problem.

A typical chatbot can lack this feature. Even if a ChatGPT can help with summarization, some information may be irrelevant.

6. Ticket triage

It is only possible for your internal service desk team to learn who is eligible to handle a particle case. When RAG has access to that resource, your Private ChatGPT can allocate a particular employee support case to the right agent when needed.

An LLM inside a Generative AI interface uses RAG and LLM to build predictive insights and determine who can handle a case depending on previous history and actions taken.

However, a public version of the GPT model cannot work to this scale due to limited data exposure, giving only a generic solution.

7. Accurate and relevant answers

Employees are more independent in solving IT and HR-related queries rather than choosing to go to agents. The Retrieval Augmented Generation, or RAG, helps boost LLM searches and fetch relevant answers, which offer intent, context, and disambiguation. This can effectively remove hallucinations, which are familiar with the public version of the GPT model, as they can make up responses in the absence of sufficient data.

Employees can solve problems faster and increase productivity with an increased relevancy rate.

Workativ is known to make employee support seamless with its LLM-powered conversational AI platform.

In addition, Workativ offers an excellent opportunity to build a private ChatGPT effortlessly using its Knowledge AI feature.

Knowledge AI is similar to RAG systems, allowing you to access third-party resources to extend LLM searches.

Workativ is a no-code SaaS-based conversational AI platform that can easily be used to build your Private ChatGPT for HR and IT support.

Follow some simple steps to build your private ChatGPT with Workativ:

Step 1: Use existing conversations to add Knowledge AI

• Choose an existing conversation flow

• Click the Arrow sign next to a card

• Find a drop-down menu

• Select Knowledge AI

• Supply details to the box

• Save it

Your existing chat conversation can leverage the power of Knowledge AI or private ChatGPT-like searches and boost employee support.

To learn more about how you can create a Private ChatGPT for employee support, contact us at Workativ.

Step 2: Build Knowledge AI conversations from scratch

• Go to chatbot builder inside Workativ

• Select Knowledge AI from the menu header

• Select third-party resources, i.e., SharePoint, Drive, DropBox, etc

• Supply necessary information and get started

• Prepare user query

• Customize your workflows

• Add human touch

• Deploy your Knowledge AI or private ChatGPT bot in MS Teams, Slack, or anywhere

Building a private ChatGPT is not hard work. You can use a zero-code platform and implement Knowledge AI search for your employees.

As you build your private ChatGPT or Knowledge AI chatbot, things can work in your favor, and you will have amazing benefits for HR and IT support.

Zero-wait time

Private ChatGPT or Knowledge AI for HR chatbot or IT support chatbot can boost collaborations and communications by turning your knowledge articles into ChatGPT-like responses. Your employees can increase their response time for problem resolutions and enjoy zero wait time for service desk queries.

Zero-touch service desks

Workativ’s Knowledge AI or Private ChatGPT empowers employees, including service desk agents. With LLM-powered knowledge articles, your employees find answers rapidly, seeking agents’ help in rare cases. Employees autonomously solve their problems, freeing agents to drive towards zero-touch service desks.

Relevant answers and zero hallucinations

RAG or Knowledge AI pulls information from third-party resources to give intent-based and contextual custom responses. This reduces the chance of LLM hallucinating due to a lack of sufficient proprietary data. RAG leverages semantic search to help employees get accurate and relevant answers.

Increased agent efficiency

Service desk agents leverage RAG-based chatbot support to accelerate the pace of service delivery for employees by using conversation history, AI-based suggestions, or AI summary capabilities. This allows agents to handle as many tickets as possible and reduce MTTR.

Enhanced reporting and analytics

Workativ’s Knowledge AI chatbot is highly effective for reporting and analytics. It enables you to harness rich data across various components that help you view actionable insights into agent performance and ticket status. So, your service desk agents can prepare to deliver enhanced ITSM services and elevate user experience.

Cost-efficiency

Zero-wait time, zero-touch service desk, and intent-based answers prevent tickets from escalating to the service desk. Ticket counts decrease gradually, and agents can solve more critical tickets in less time. All these ultimately help reduce costs in managing service desk operations and boost business cost-efficiency.

RAG is the best approach to overcoming the limitations of the public version of LLM-powered GPT models or ChatGPT. The other way around, it helps you build your private ChatGPT, which helps you extend automation, reduce friction from self-service, and increase employee adoption for enhanced efficiency and productivity.

In addition, Workativ has a flexible way for your service desk to leverage RAG, which helps you gain the benefits of private ChatGPT without any extra effort.

Build your private ChatGPT with us. Schedule a demo today.

1. What is a private ChatGPT?

A private ChatGPT extends the limitations of the public version of GPT and improves LLM searches for custom responses for domain-specific scenarios by retrieving inputs from third-party resources.

2. How can you relate a private ChatGPT to a RAG-based chatbot?

A private ChatGPT enables custom responses through integration with the third-party data repository. The RAG approach, or Retrieval Augmented Generation, enables integration with third-party resources, enables hybrid NLU for added LLM searches, and helps you build a private ChatGPT.

3. What are the benefits of a private ChatGPT for employee support?

Service desk managers or IT leaders can benefit from leveraging a private ChatGPT, which ensures relevant answers, zero hallucinations, increased agent efficiency, empowered employees, etc.

4. How does Workativ help you build a private ChatGPT?

Workativ provides a no-code RAG-based platform or hybrid NLU, Knowledge AI, to help you build your private ChatGPT. Knowledge AI has an easy-to-use interface for integrating your third-party resources and intranet or company website to enhance custom capability and the power of LLM searches for user queries.