The Power of Self-Service: Empowering Employees with a Private ChatGPT

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.

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.

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.

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.

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 featur

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.

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.

• 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.

• 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.

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.

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.

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.

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.

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.

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.