How does an AI knowledge base differ from a traditional one?

Traditional and AI knowledge bases are repositories of information designed to help users find answers to their questions. While traditional knowledge bases have long been essential for storing and organizing information, the introduction of AI has significantly enhanced their functionality. Here are some of the key differences between the two approaches.

Functionality

While a traditional knowledge base is a passive system that stores information for retrieval by a user, an AI knowledge base actively uses artificial intelligence to understand user queries and provide context-aware answers.

While users must scour a traditional knowledge base to find relevant information, an AI knowledge base can do the legwork for them.

Automation

AI knowledge bases can autonomously infer insights, organize content, and update themselves based on new data. Traditional knowledge bases, meanwhile, rely entirely on manual input and updates, making them less efficient for dynamic environments.

AI-powered knowledge bases learn and improve over time through machine learning in response to user interactions and feedback. Traditional systems are static, requiring manual effort to refine and adapt to new information or use cases.

Adaptability

AI systems use advanced technologies like NLP to understand the context behind search queries. Traditional knowledge bases typically require exact matches or keyword searches, limiting their ability to handle complex queries.

Personalization

AI knowledge bases facilitate personalized self-service thanks to powerful, context-based searches and content recommendations, compared to traditional knowledge bases, which rely entirely on the user knowing what they’re searching for.

While a traditional knowledge base typically displays information exactly as it’s stored, an AI knowledge base uses NLP to respond to user queries in conversational language

Interactivity

AI knowledge bases often include interactive interfaces like chatbots or virtual assistants. These enable users to engage in dynamic, conversational interactions and provide a more engaging user experience. Traditional knowledge bases typically offer limited interactivity but use simple search functionalities instead.

Applications

AI knowledge bases are well-suited for customer support, intelligent virtual assistants, and real-time decision-making applications. Traditional knowledge bases, in contrast, are more often used for straightforward documentation or reference purposes.

Why is an AI knowledge base necessary?

AI knowledge bases are necessary because they enable efficient, intelligent, and dynamic access to information in complex and fast-paced environments.

Unlike traditional systems, AI-powered knowledge bases can process unstructured data, understand natural language queries, and provide context-aware answers. They also adapt over time, improving their accuracy and relevance.

All this makes them an ideal solution in situations where quick and precise responses are critical, such as customer support or decision-making processes.

AI knowledge bases are also useful for automating routine tasks, reducing the manual effort human employees need. They’re also highly scalable, allowing organizations to manage vast information effectively. 

Benefits of AI knowledge bases

Implementing an AI knowledge base can bring various benefits to any organization.

Improved efficiency

AI knowledge bases automate repetitive tasks, such as retrieving information, categorizing data, and answering common queries. This reduces employees' time searching for information, allowing them to focus on higher-value tasks.

On the customer side, because AI-powered systems can automatically categorize and index new information, users will always have fast access to the latest data.

Greater accuracy

By leveraging NLP, text analysis, and advanced algorithms, AI knowledge bases are better placed to understand queries accurately. They can analyze context and intent, reducing the number of errors when retrieving information and delivering precise, relevant responses tailored to each user's specific needs.

Improved scalability

AI-powered systems can manage massive amounts of structured and unstructured data without compromising performance. As organizations grow, these systems can easily accommodate new information, making them suitable for businesses of all sizes and industries.

Enhanced adaptability

AI knowledge bases continuously learn from new data and user interactions, refining their responses and updating their knowledge automatically. This ensures they remain effective even as information evolves, unlike static systems that require frequent manual updates.

Greater accessibility

With an AI knowledge base, users can access information quickly and easily from anywhere, at any time. Many modern AI knowledge bases are cloud-based, meaning that users can retrieve information using any internet connection device. This is particularly beneficial for organizations that employ remote teams.

Moreover, AI knowledge bases are always active, ensuring users can access information or support at any time, regardless of the time or location. This benefits global organizations especially, enabling customers to access assistance despite time zones and language barriers.

Improved user experience

AI knowledge bases power conversational interfaces like chatbots and virtual assistants, which users can interact with naturally as if talking to a living, breathing person. This intuitive experience reduces users' frustrations from interacting with a cold, calculated machine and improves customer engagement by delivering answers in real-time.

Faster decision making

AI knowledge bases analyze data and provide insights almost instantly. Whether it’s identifying trends, solving complex problems, or providing analytics, this speed supports quicker and more informed decision-making for customers and businesses.

Cost savings

Automating tasks and reducing the need for 24/7 human support staff availability means that AI knowledge bases can greatly reduce operational costs. They also minimize the number of errors and inefficiencies in the support process, which can lead to significant cost reductions over time.

Opportunities for integration

AI knowledge bases offer integrations with other systems, such as customer relationship management (CRM) tools, enterprise resource planning (ERP) software, and data analytics platforms. This helps to create a unified workflow throughout the organization, improving productivity and ensuring information is readily available across departments.

Different kinds of AI-powered knowledge base content

AI-powered knowledge bases hold information from several different categories of content. Combined, these diverse forms of data allow businesses to enhance their customer support, improve their operational effectiveness, and streamline their decision-making processes.

Structure knowledge content

Structured knowledge content includes information that’s formatted systematically. It covers well-organized and easily navigable content, such as articles, FAQs, manuals, and guides.

This information often forms the foundation of the knowledge base and can be used to provide clear answers to the most common queries.

It’s usually arranged into a strategic hierarchy, with the most useful and relevant information at the top and less useful content relegated further down.

AI knowledge bases easily retrieve and present structured knowledge content thanks to standardized formats, consistent terminology, optimized keywords, and systematic categorization. 

Unstructured knowledge content

Unstructured knowledge content refers to information that lacks a specific format or predefined structure. This can include emails, chat transcripts, social media interactions, and multimedia files.

Unstructured content is often more challenging to organize and analyze because it comes in many formats and needs a standardized structure. Despite this, it's beneficial to have unstructured knowledge content in an AI-powered knowledge base as it can add great depth and variety.

NLP and ML algorithms are essential for extracting insights and meaning from unstructured data, with text and sentiment analysis extracting insights and making this information accessible and valuable.

Automated knowledge content

Automated knowledge content is any information generated or curated automatically by knowledge AI algorithms. This can include automatically generated suggested responses to common customer queries, summaries of documents, or recommendations for improving existing content.

Automated knowledge content is continuously updated and refined based on user interactions and feedback and can help to ensure relevance and enhance accuracy and efficiency within the knowledge base.

Types of AI knowledge base and examples

AI knowledge bases come in various types, each designed for specific use cases. Here are some key types, along with some examples of each.

Rule-based knowledge base 

Rule-based knowledge bases are one of the most structured forms of AI knowledge systems. They use predefined rules and logic to make decisions, draw conclusions, and provide answers. These rules are typically written in an "If-Then-Else" format and are based on domain expertise.

Medical diagnosis systems like MYCIN, used for diagnosing bacterial infections, are a prominent example of rule-based knowledge bases. They’re ideal for this use case because their knowledge is encoded explicitly as logical statements, e.g., "If symptom ‘A’ and symptom ‘B,’ then diagnosis ‘C’."

Semantic knowledge base

A semantic knowledge base is perfect for situations that require an advanced understanding of the underlying meaning of an input. Semantic search determines the user intent, context, and conceptual meanings behind queries.

These knowledge bases are ideal in customer service use cases, where even the most vague requests must be correctly interpreted and answered quickly. 

Natural language processing knowledge base

NLP knowledge bases specialize in both interpreting and generating human language. They can understand customer queries, regardless of the language style or syntax used or whether the query was typed or spoken.

This makes them effective for handling diverse demographics and query types in customer service scenarios. They’re also used by virtual assistants such as Amazon’s Alexa and Apple’s Siri, where NLP is used to interpret spoken language, query a knowledge base, and provide relevant responses to queries such as "what’s the weather like today?," or "where’s the nearest gas station"?

Machine learning knowledge base

ML-enhanced knowledge bases use ML algorithms to evolve in response to customer interactions and feedback. They can also extract, organize, and retrieve knowledge from vast unstructured data.

One example is the Microsoft Dynamics 365 Knowledge Base, which uses ML to power intelligent customer support and service delivery. Support ticket patterns are analyzed using ML, allowing the AI to predict relevant solutions and optimize the knowledge base by identifying frequently accessed content. This means the best resolution for an issue can be suggested based on historical data, and useful articles can be highlighted for agents responding to specific queries.

Hybrid knowledge base

A hybrid knowledge base combines multiple knowledge representation methods and reasoning techniques, leveraging the strengths of each technology to create a powerful and versatile knowledge base. They’ll usually integrate rule-based systems, ontologies, and machine learning models, allowing for deterministic reasoning using explicit rules and probabilistic learning using pattern recognition and adaptability.

Examples of hybrid knowledge bases include OpenAI’s GPT models, which combine pre-trained ML models with domain-specific knowledge bases for enhanced performance. GPT handles natural language generation and interpretation using ML, while a rule-based or curated knowledge base ensures accuracy and domain relevance for specific applications.

Case-based knowledge base

A case-based knowledge base is a system that stores specific examples, or ‘cases’, of problem-solving experiences and uses them to resolve new problems. It relies on case-based reasoning (CBR), which draws on past cases to solve similar scenarios.

This makes case-based knowledge bases especially useful in domains where problems are recurring and context-specific. One example is clinical decision support systems (CDSS), which assists healthcare providers by offering treatment suggestions. They can analyze patient symptoms, match them to historical cases, and suggest evidence-based solutions.

For instance, a CDSS could recommend a treatment for a patient with rare symptoms by referencing a previous similar case in its database.

Deep learning knowledge base

A deep learning knowledge base stores vast amounts of unstructured data, including images, videos, and audio, and uses neural networks to learn complex patterns and associations among disparate data. This knowledge base is typically used for more advanced tasks such as image recognition, speech recognition, and natural language understanding.

Google DeepMind is one example of a deep learning knowledge base. It uses deep learning to interpret complex data and generate intelligent responses or predictions.

Key components of an AI knowledge base

Several key features of AI knowledge bases can help users save time and effort and improve the overall user experience. Here are some of the key features you should know about.

Machine learning models

These algorithms enable the AI to learn from data, identify patterns, and make predictions without human intervention. 

Natural language processing

NLP enables AI to understand and interpret human language. This is essential for analyzing and responding to user queries, as it can understand the context and intent of the user's search query.  

Data repository

A centralized storage system where all the relevant data is held. Both structured data like databases and unstructured data like text documents or multimedia files are stored here. 

Knowledge representation

Knowledge representation defines how knowledge is organized, stored, and accessed in the system and provides a structured way to store information for efficient reasoning and querying.

Common approaches to knowledge representation include:

• Ontologies represent relationships between entities (e.g., "bread" is a type of "food").

• Rules apply "if-then" logic for deterministic reasoning (e.g., if symptoms are  "A" and "B" then diagnosis is "C").

• Semantic networks are graph-based representations showing relationships between concepts.

Reasoning engine

A reasoning engine performs logical operations on stored knowledge to derive conclusions or make decisions.

Knowledge management tools

Knowledge management tools are the interfaces and processes for curating, organizing, and updating the knowledge base.

Integration layer

The integration layer connects the knowledge base with external systems, applications, and APIs. This enables an AI knowledge base to integrate directly with IoT (Internet of Things) devices, CRM (customer relationship management) platforms, and more.

AI-powered full portal search

This interface allows users to search the knowledge base with AI through an index of every article in the knowledge base. 

Content summarizer

A content summarizer lets users quickly condense lengthy content into a few sentences, making it easy to distill content to its key points when relaying information to customers. AI understands the meaning and context of content and cuts it down to improve the user experience.

Related content recommender

AI can enhance content discoverability through related article recommendations. These automatically suggest more content to users based on their access.

Tag recommender

Organizing and tagging data is crucial for making it findable. A tag recommender helps the knowledge base admin to add relevant tags and efficiently organize content at scale.

Meta description generator

AI can even generate meta descriptions for data sources, making them more discoverable and improving SEO indexing and general accessibility.

How to build an AI knowledge base in 10 steps

Now that you know the basics about AI knowledge bases, it’s time to learn how to build one of your own. Here’s our step-by-step guide for getting started.

1. Define your objectives

The first step is to identify your AI knowledge base's specific goals and scope. Knowing exactly what you want your knowledge base to do will help guide you at every step of its creation.

You should define your knowledge base's purpose, target audience, and expected outcomes at this stage, paying close attention to whether it’s intended for internal use among stakeholders or for external use by customers.

2. Choose your knowledge base software

Next, research and select a suitable AI-powered knowledge base software platform. Plenty are available, and choosing one that aligns with your defined objectives is important. (We’ll cover key things to look for in a later section).

3. Gather and preprocess data

It's time to gather the data that’ll power your AI knowledge base. Collect relevant data and preprocess it by cleaning, organizing, and structuring it for AI analysis. Pay close attention to the quality and accuracy of your data. The more reliable it is, the more effective your knowledge base will be.

4. Select the correct model

You’ll have to decide on the structure of your knowledge base and categorize and tag your data so it can be efficiently retrieved. Choose between a structured or unstructured format based on the types of data valuable to you and your users' preferences. 

5. Create high-quality content

Create content to populate your knowledge base. Any content created should be high-quality, accurate, relevant, and regularly updated to ensure the integrity of the knowledge base is upheld. Articles, guides, frequently asked questions, and tutorials are all great examples.

6. Implement your AI model

Now, you can implement the AI aspect of your knowledge base. Adding NLP and ML into your knowledge base software can supercharge its effectiveness.

7. Test and refine

Once your knowledge base is up and running, you should evaluate its performance to ensure it works as intended. Test its accuracy, responsiveness, and usability across different scenarios and customer interactions, and gather user feedback to identify improvement areas.

8. Integrate AI-powered tools

Adding extra AI-powered tools, such as generative AI, will increase your systems’ capabilities. A generative AI knowledge base improves knowledge management by dynamically creating and organizing information. It can generate concise summaries, answer complex queries, and provide tailored content based on user needs, saving time and increasing efficiency. Generative AI also simplifies the creation of richer and more natural conversations and results for your customers, providing them with an elevated user experience.

9. Make it user-friendly

Ensure a great user experience by developing an easy-to-navigate, user-friendly interface. You can boost usability and user engagement by prioritizing intuitive design, optimized search functionalities, and personalized user experiences.

10. Monitor and improve

Continuously monitor the performance of your AI knowledge base going forward to ensure continued success. Implement analytics and track metrics such as content usage and model accuracy to help you identify what can be improved. Wherever possible, gather feedback from internal and external users to help you optimize your knowledge base.

How to choose AI knowledge base software

The type of AI knowledge base software you choose will largely depend on the specific needs of your organization and the goals you’ve laid out. That being said, a few key factors should always be considered when making a decision.

Ease of use

Any tool or technology implemented in a business needs an intuitive interface that any employee can use, regardless of skill level or technical know-how. The easier the tool is to use, the easier it will be for users to find and manage information.

A user-friendly design makes the learning curve much less steep, encouraging adoption and improving time to value. Features like straightforward navigation, customizable dashboards, instructional videos, and webinars contribute to an easy-to-use platform.

Scalability

Chances are your organization will be looking to grow at some point in the future, if not right now, so it’s important to choose tools that can scale alongside you. As an organization grows, so does the amount of data it has to deal with. Your knowledge base, therefore, needs to accommodate an increased number of users, larger volumes of data, and more complex queries.

Customization

Every organization is different, meaning the types of data they amass and queries they have to deal with are different. This makes customizability a highly attractive trait among knowledge base platforms.

The best software will allow you to customize the categorization of information, search functions, and user interfaces to suit your specific needs.

Support and training

No matter how easy your knowledge base software is to use, there will come a time when you might need a little help getting the most out of it. Ensure the platform provider offers robust support and training resources for those occasions.

Comprehensive onboarding is a great place to start, as this will help you get up to speed with your software quickly and reduce the time it takes to start seeing its value. The best providers will offer training resources like tutorials and webinars to help you get the most from your software.

Regular updates are another crucial factor in ensuring that your software is future-proof. Finally, a responsive customer service offering will ensure that any problems you have are solved quickly, reducing your downtime.

Integration

Choosing a platform that integrates seamlessly with your existing systems and tools will prevent you from altering your tech stack to accommodate your new software. The right knowledge base AI will complement your current workflows rather than disrupt them.

Ensure any platform you choose supports integration with the business applications you’re already using, including CRM systems, project management tools, and communication platforms.

What features should you look for in AI knowledge software?

When choosing AI-powered knowledge base software, it’s essential to prioritize features that align with your organization's needs for knowledge management, accessibility, and scalability. Here are some of the critical features to consider:

• Intelligent search helps users to find information quickly and accurately. AI-powered semantic search understands context, not just keywords, and can search within multiple content types, including text, images, and videos.

• Multimedia support allows users to access knowledge regardless of its format. Support for text, images, videos, audio, and PDFs is crucial. AI tools for indexing multimedia, e.g., video transcription, are also beneficial.

• Collaboration tools encourage team engagement and knowledge sharing. The ability to annotate and post comments on content improves discoverability and fosters a collaborative environment.

• Analytics and insights help measure the effectiveness of the knowledge base and identify potential knowledge gaps. Usage metrics, such as most-searched or most-viewed content and user engagement tracking, can help to distinguish high-value content from less usable content.

• Content creation and editing tools help users keep the knowledge base up to date with minimal effort. Templates for common content types, like FAQs or guides, are useful when creating content as they help keep things uniform and ensure consistent quality.

• Mobile accessibility ensures that the knowledge base can be accessed even when moving. With an increasing number of employees working remotely, a fully responsive design for mobile devices and mobile apps for offline access has never been more vital.

• Security and access control features help to protect sensitive information and ensure appropriate access. Role-based access controls restrict content visibility, and data encryption ensures secure storage and transmission, as well as compliance with relevant regulations and laws, like GDPR or HIPAA.

• Cost-effectiveness helps to align the software with budgetary constraints while still meeting needs. Look for transparent pricing models and options to scale features based on business size and goals.

Combine top communication capabilities with an AI knowledge base for an excellent customer experience

In today’s fast-paced business world, delivering exceptional customer experiences is crucial for retaining loyalty and driving growth. Combining top-notch communication tools with an AI-powered knowledge base creates a powerful synergy that enhances support efficiency, customer satisfaction, and brand reputation.

With advanced AI capabilities, knowledge bases provide instant, contextually relevant answers by understanding user queries through semantic search and natural language processing. When integrated with communication tools like chatbots, live chat, or email systems, they empower support teams to resolve issues faster and more accurately.

If you’re looking for a communications solution that will allow you to make the most of your AI knowledge base, then look no further than the Vonage Communications Platform. It’s been built with conversational AI in mind, boasting speech-to-text models and a Natural Language Understanding (NLU) engine that will help you to implement AI seamlessly across your organization.

Vonage AI Studio enables businesses to create conversational AI solutions, such as chatbots and virtual assistants, to automate repetitive tasks like scheduling interviews, answering FAQs, and engaging with candidates so that you can supplement your AI knowledge base with an automated suite of support options.

Vonage also offers communications APIs for voice, messages, and video, enabling seamless, omnichannel communication. Whichever AI knowledge base software you choose, you can integrate Vonage to streamline your workflows and save your support agents time and energy.

Learn more about Vonage Communications APIs now.