AI and knowledge management: Why RAG is essential

For enterprises looking to stay ahead of the curve, effective knowledge management is vital. Yet, many organizations find themselves hampered by outdated and inefficient systems.

Knowledge management (KM) is the set of processes used by an organization to create, analyze, and share information with its stakeholders in a fast, secure, and reliable way. Examples of knowledge management systems used by companies may include:

• FAQ pages
• Internal training courses
• Chatbots
• Webinars
• Internal company social networks (such as Yammer)

Despite the wide array of tools enterprises can choose from, KM can be notoriously tricky to get right. FAQ pages may be incomplete or outdated. Webinars may be expensive to create, and you can’t guarantee that the employees who need them most actually view them. In all cases, regardless of industry, people still need to jump through several hoops to find the information they need.

Arguably, one of the most common and well-known use case category for GenAI is retrieval-augmented generation (RAG). RAG’s attractiveness is due to its immense potential for transforming knowledge management in enterprises.

Popular, consumer-facing GenAI applications (such as ChatGPT or Gemini) have shortcomings that make them unsuitable for enterprise KM. These large language models (LLMs) are only aware of the state of the world up to their training cutoff date, and they are only trained using publicly available data. As a result, they cannot answer questions about private or internal information to a particular organization.

Enter RAG, a technique used to enable LLMs to answer questions using data fetched from external sources in real time. RAG-enhanced GenAI applications allow enterprises to leverage an LLM’s incredible content summarization and question-answering capabilities with data that it was not originally trained on. This makes RAG a truly transformative tool for managing enterprise knowledge systems.

Embracing AI in knowledge management systems

While companies have access to countless tools to streamline their KM efforts, stakeholders face challenges with fetching and consuming the right information. These challenges include:

• Limited adoption of advanced KM tools: Many tools have a learning curve that hinders their adoption by the target audience. In addition, these tools require users to develop a habit of incorporating them into everyday workflows.
• Siloed information and lack of integration: Individual KM tools typically work in isolation, requiring stakeholders to juggle multiple tools while also knowing which tool to use for each particular use case. Consider the example of a product manager who needs to look at Tableau for the latest growth numbers, Confluence for feature documentation, and Salesforce for CRM metrics.
• Inconsistent and outdated knowledge bases: Many KM tools require manual updating to remain useful and relevant, but making these updates is often a low priority.
• Lukewarm satisfaction with existing KM solutions: Enterprise tools are notorious for not being top-notch when it comes to user experience. This leads to only a lukewarm reception in most cases.
• Difficulties in measuring ROI and demonstrating value: KM tools accrue costs, and the value they create is not always readily apparent to stakeholders.

GenAI and RAG are perfectly poised to tackle the challenges associated with traditional KM tools. As a result, we’ve seen a flurry of RAG-enhanced AI tools that have the potential to revolutionize KM across industries. Some of the ways these tools are making an impact include:

• Intelligent search and retrieval: RAG-based systems ensure that the correct information is brought to you rather than forcing you to search for it. They can understand queries in natural language and find the correct source for the answer. By deducing user intent and context-specific meanings, they ensure greater accuracy, even when dealing with jargon.
• Content summarization: LLMs can summarize lengthy documents, answer a question while citing the document, and offer key points, themes, and details.
• Data tagging and classification: AI systems can classify internal documents while deducing patterns, topics, and relationships between documents.
• Automation: Arguably the most well-known use case, GenAI applications can automate the execution of repetitive tasks, and do so in response to natural language commands. Companies today are leveraging such systems to perform tasks such as data entry, content creation, and report generation. 

How RAG enhances knowledge management

At a fundamental level, RAG-enhanced AI systems typically surpass their traditional KM counterparts in the following ways:

• Knowledge discovery: RAG improves the accuracy and relevance of information retrieval while considering user intent and context-specific meanings. This makes it significantly easier to find valuable information, as the burden of search is put entirely on the AI system.
• Knowledge sharing: A well-designed RAG system can leverage all the data and knowledge available across diverse sources to supercharge finding valuable insights. RAG improves the accuracy and relevance of information retrieval by extracting the right information from the right sources and synthesizing all extracted knowledge into actionable answers.
• Decision making: RAG systems can deliver comprehensive and accurate insights, enhancing data-driven decision-making processes.

Real-world use case #1: Customer support for a SaaS tool

RAG systems are ideal for customer support across various industries. In the case of a SaaS company, prospective and current customers may have a number of questions regarding the company’s offerings, such as "What are the pricing tiers for the SaaS offering?" or “How do I solve an issue that I’m facing with the tool?”

At present, the customer would first need to discover the source that can answer their question, whether that’s the main website, a blog post, documentation, an open GitHub issue, or elsewhere. 

However, with RAG, the SaaS company can simply offer a chatbot that can deduce the intent of the question, identify and extract the correct information, and provide an answer that is correct and to the point. The system can achieve this by incorporating all the diverse data sources listed above and more.

Real-world use case #2: Information retrieval

In legal firms and the corporate social responsibility (CSR) divisions of enterprises such as banks, knowledge workers typically need to hunt for answers by searching documents and thousands of pages. Using a RAG system and a well-designed vector database for semantic search, these enterprises can reduce the time it takes to summarize large PDF documents and answer questions to just a few seconds.

Benefits of using RAG to enhance knowledge management

By implementing a GenAI system that leverages RAG, you set your organization up for success, ensuring the following:

• Efficient information handling: The RAG system handles the bulk of the search effort, effectively reducing the time stakeholders take to find information.
• Reducing knowledge silos: The RAG system can integrate disparate and independent sources of information into a cohesive system, improving accessibility, reliability, and consistency.
• Personalized and contextual information: The GenAI application can deduce context and user intent to deliver tailored insights based on conversation history, role in the organization, and specific needs.
• Scalability: RAG allows you to scale KM as the number of stakeholders and knowledge increases. Unlike traditional KM tools, RAG-powered systems ensure information is always accurate, up-to-date, and available.

3 Strategic considerations for implementing RAG

RAG systems can be extremely powerful, but while determining how to implement retrieval-augmented generation and their new AI knowledge management strategies, enterprises need to keep the following things in mind:

• Integration with existing systems: The RAG solution should seamlessly integrate with the organization's current KM tools and infrastructure. This will simplify the task of integrating diverse data sources.
• Managing information quality and governance: One of the most attractive features of RAG is its ability to provide an LLM [em1] with proprietary or confidential data. However, this means that enterprises must work to maintain data integrity, comply with data protection regulations, and implement robust data governance frameworks. This is especially important for industries in highly regulated spaces such as healthcare and finance.
• Organizational learning and change management: Despite their incredible adoption rates, LLM-based tools are still a nascent piece of technology that may be foreign to most people in your organization. It is important to prepare your workforce for this new technology through training programs and effective change management strategies. Enterprises need to ensure that stakeholders are aware of what AI-powered KM tools can and cannot do, along with ethical use guidelines, and how to report incidents such as data leaks or inaccurate responses.

Lead the transformation of your enterprise knowledge management

RAG has generated much hype and excitement around its capabilities and potential use cases. Among the most promising retrieval-augmented generation use cases[al2]  is the transformation of enterprise KM. By adopting a knowledge management strategy[al3]  that revolves around RAG-enhanced, AI-powered tools, your enterprise can dramatically increase the efficiency of internal stakeholders and the customer experience.

Outshift by Cisco is an industry leader in partnering with enterprises on their GenAI innovation journey. It offers a wealth of expertise and resources to help your organization be successful as it pursues applications that leverage cutting-edge technologies like LLMs and RAG. Learn more about what Outshift is doing in the GenAI space by checking out these resources:

• 6 steps to assess AI readiness in your enterprise
• Why we care about trustworthy and responsible AI