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Author
Konrad Heimel
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The advent of Large Language Models (LLMs) has revolutionized various industries and changed  IT support in particular. Since the groundbreaking launch of GPT-3, a powerful LLM, in June 2020, LLM technology has evolved rapidly and become increasingly accessible and sophisticated. Automating IT support is an obvious use case, but the complexity of implementing customized solutions has been prohibitive. This has now changed, opening new possibilities for knowledge management and IT Service Management (ITSM).  

In the current business environment, there is an urgent need for efficient and cost-effective IT support. Chatbots powered by Retrieval Augmented Generation (RAG) are here to revolutionize the way you handle user queries. With RAG, chatbots can access existing knowledge bases, so they don't need additional training. This means faster setup, quicker responses for your users and happier employees who can focus on more difficult IT challenges. A win-win situation for efficiency and cost savings.

We've created a user-friendly demo with Microsoft Azure AI to show how easy it's to deploy powerful chatbots. This demo illustrates the potential of Retrieval Augmented Generation (RAG) technology to revolutionize IT support. In this article, we dive into the development of chatbots, explore the mechanics of RAG and present real-world use cases in IT service management. We also present the tangible benefits of our own managed service chatbot and prove that it can save you time and money.

How Does RAG Work?  

Large Language Models (LLMs) like the GPT series are incredibly powerful but have some limitations. They might not know about specific topics if they haven't been trained on that data. For example, they might not have the answer if asked about the latest updates in a niche software released after their last training data. Another issue is “hallucination,” where the model generates plausible responses that are inaccurate or misleading. For instance, an LLM might fabricate details about a software feature if it doesn't have the correct information.  

One way to fix this is to fine-tune the model with specific data. However, this can be time-consuming and costly. A simpler and more efficient method is the Retrieval-Augmented Generation (RAG) pattern.  

Boost efficiency & accuracy: The power of RAG-powered chatbots

RAG is a way to enhance what Large Language Models know by incorporating additional data. For example, an IT support chatbot can query historical ticket information or external websites to provide accurate and current responses.

It consists of two main components:  

  1. Indexing: This involves taking data from various sources and organizing it so that the system can easily use it.
  2. Retrieval and Generation: This two-step process works as follows:  
  • Retrieval step: When a user submits a query, the system first searches a dedicated data store or index to find relevant documents or data snippets. This retrieval mechanism ensures the model can access the most pertinent and updated information.
  • Generation step: The retrieved information is then combined with the user's query and fed into the LLM.  

Using this augmented input, the LLM generates a response that is informed by the latest data and formulated in a coherent, human-like manner.  

Put simply, RAG helps LLMs gain more knowledge by gathering additional information to better answer questions. This approach eliminates the need for complex fine-tuning and retraining of models, making the implementation easier and cost-effective.

Workflow of a Gen AI-enabled RAG system with Azure AI SearchFigure 1: Workflow of a Gen AI-enabled RAG system with Azure AI Search. The user query is processed by an app server, which queries the Azure AI Search Vector Database. Retrieved documents are passed to a large language model to generate a comprehensive response.

ITSM chatbots and use cases  

LLM-supported chatbots, especially in combination with the RAG pattern, offer several compelling use cases in IT Service Management (ITSM). They can revolutionize various aspects of IT support, including service desk, user and application support, knowledge management, and incident management.  

IT support / service request management  

ITSM chatbots can automate responses to common customer support queries, significantly reducing the workload of support staff. Take, for example, a scenario in which an employee needs to introduce a new application. The chatbot can guide the user through the process step by step, providing relevant information and instructions at each stage. This saves the employee time and takes the pressure off the support staff when dealing with more complex problems.

Knowledge management

Chatbots revolutionizing knowledge management by enabling uniform access to various information sources such as Confluence, external websites, PDFs, and PowerPoint presentations. They enable efficient search and discovery through keyword and contextual searches and can summarize long documents for ease of use. Chatbots also help maintain an up-to-date knowledge base by identifying frequently asked questions and prompting the creation or updating of articles. Chatbots significantly enhance efficiency and user satisfaction by providing a user-friendly interface that answers queries in natural language and provides step-by-step guidance.  

Incident management  

In incident management, ITSM chatbots can suggest resolutions based on historical data. When a user reports an issue, the chatbot can search past incidents to find similar cases and suggest proven solutions, reducing the time required to resolve incidents. Moreover, chatbots can collect important information about the incident and automatically fill out the incident ticket, to streamline the process. For example, a chatbot can ask the user for details about the issue, such as error messages or recent changes to the system, and record this information.  

Low-hanging fruits: Immediate benefits  

While the potential applications of ITSM chatbots are vast, some use cases are easy to implement and offer immediate benefits, such as automating L1 support and enhancing knowledge management. Implementing chatbots in these areas can lead to quick wins, including the reduction of repetitive tasks for support staff, faster response times, and improved customer satisfaction. These benefits can be realized with minimal effort and investment, making them an attractive starting point for organizations looking to deploy ITSM chatbots.  

Saperion Support Chatbot

As a part of our exploration of the possibilities of large language models (LLMs) and the Retrieval-Augmented Generation (RAG) pattern, we developed a chatbot to support the Saperion Enterprise Content Management tools, one of the applications in our managed services portfolio. The data used for this chatbot included a set of anonymized historical tickets and various technical documents in formats such as PDF, Word, PowerPoint, and text files. Using Azure AI Search, we created a hybrid vector and semantic search index and hosted the chatbot front-end on Azure Web Apps.

Screenshot of the Saperion chatbot demo showcasing a common user query and the chatbot's responseFigure 2: Screenshot of the Saperion chatbot demo showcasing a common user query and the chatbot's response.  

Key functionalities of RAG

The chatbot demo showcased the following capabilities:  

  • Accurate query response: The chatbot demonstrated a high level of accuracy and comprehensiveness in answering common user queries. Based on our observations, we are confident that it can solve at least 70% of user requests without human intervention.
  • Document referencing: It references to relevant technical documents when answering user queries.
  • Solution suggestions: The chatbot suggests solutions based on the content of the technical documents.
  • Scope limitation:  No attempt was made to answer questions that were outside the scope.

A look at the implementation highlights & takeaways

Our experience proves that developing a solution that delivers excellent results takes little time, requires minimal effort and yet offers significant benefits. Here are some key aspects:

  • Time efficiency: This chatbot demo was successfully implemented in less than a week. This demonstrates the potential of LLMs with the RAG pattern, and the low costs and barriers to entry for implementing such a solution.
  • Positive reception: Members of the support team appreciated the chatbot's ability to significantly reduce the time spent on repeatedly answering common questions. They anticipate that users will find it highly beneficial for quickly locating relevant information, without the need to sift through extensive documents.
  • Cost-effective: The infrastructure costs for hosting the chatbot were only a few hundred dollars per month.  

The path to a production-ready solution

Several enhancements are required to develop the chatbot into a production-ready solution:

  • Integration with existing systems: Integrate the chatbot with the current ticketing system and knowledge base for automatic ticket responses and continuous updates to the search index.
  • Seamless communication tools integration: Incorporate the chatbot into existing communication platforms like Slack or Microsoft Teams, providing a more intuitive and accessible user interface than a standalone web interface.
  • Escalation to human agents: Implement an easy-to-use escalation feature for users who prefer human interaction or when the chatbot cannot provide a solution.  

These tasks are straightforward and fall into the realm of traditional software development, so they can be accomplished in a manageable time frame.

Nagarro: Pioneering Smarter Chatbots with Ginger AI

At Nagarro, we're at the forefront of chatbot innovation. Our Ginger AI chatbot exemplifies how these solutions can be seamlessly integrated into daily workflows. It acts as a central hub for a wide range of tasks, boosting employee productivity.

Ginger AI empowers employees by providing instant access to organizational information. Need HR policies or IT support steps? Ginger AI eliminates the need to sift through mountains of documents. It facilitates various processes, showcasing the practical benefits of advanced chatbot technology in action.

But Nagarro's vision for chatbots extends beyond immediate convenience. We're actively exploring the next step in chatbot evolution – the Retrieval-Augmented Generation (RAG) pattern. RAG unlocks a new level of efficiency and cost-effectiveness. It allows chatbots to access and utilize existing knowledge bases without the need for complex fine-tuning and retraining of models.

This brings significant benefits. Chatbots can provide accurate and up-to-date answers without the need for constant manual updating. Gone are the days of unreliable or outdated information. Nagarro's commitment to RAG technology ensures that chatbots are up to date with the latest knowledge, resulting in a more reliable and trustworthy user experience.

Conclusion  

AI chatbots based on Large Language Models (LLMs) and Retrieval Augmented Generation (RAG) are revolutionizing IT support. These chatbots answer user queries immediately, shorten response times and increase satisfaction. They also free your IT team from repetitive tasks and allow them to focus on more complex problems. This benefits both efficiency and usability as they pave the way for a smarter future of IT support.  

The Saperion chatbot proves how AI, powered by Large Language Models and retrieval augmented generation, can revolutionize your IT operations. This easy-to-implement solution, developed with Microsoft Azure AI, drastically reduces response times, increases user satisfaction and gives your IT team more time for strategic tasks. With Saperion, you gain efficiency and improve the user experience, paving the way for a smarter future of IT support.  

tags

Ams , Disrupt , Itsm , Servicenow

Author
Konrad Heimel
Konrad Heimel
connect
tags

Ams , Disrupt , Itsm , Servicenow