The AI knowledge base is an advancement in information management compared to traditional means of information and knowledge. The AI-based knowledge base integrates artificial intelligence technologies, including natural language processing (NLP) and machine learning (ML).
Users can easily interact with the knowledge base without reviewing lengthy resources when looking for information. These knowledge bases allow users to have answers to any queries they might have.
An important thing about the knowledge base is that it continuously improves after every user interaction. They can learn from user interactions, and thus, they improve their performance over time and offer personalized responses.
When we look at these knowledge bases, which are an advanced way of providing users with information, we see that they are significant for enhancing the user experience, scalability, and cost-efficiency. These knowledge bases achieve these goals by automating tasks and streamlining the information.
Knowledge bases leverage technologies such as NLP and ML to provide users a seamless experience. The following pointers will discuss how they typically operate.
Thus, the entire purpose of the knowledge base is to provide users with accurate knowledge representation and personalized information by retrieving information from a sophisticated system.
To build an AI knowledge base, several steps need to be followed to ensure its effectiveness.
Before you start building your AI knowledge base, define your objective. You also need to determine the purpose and scope of the knowledge base that you are creating. Consider the questions you want your knowledge base to understand and answer.
You also need to define the target audience it will cater to. Understanding this will help you guide the design you need for the development process.
For a knowledge base to be successful, it is important to gather relevant data from various sources. These knowledge bases rely on informative and well-structured content. You need to collect information from sources within your organization.
These sources may include chat logs, emails, call logs, customer feedback, or any other interaction. After gathering all the information, you need to organize it in a structured way so that Artificial Intelligence algorithms can quickly process it.
Selecting a suitable AI model is crucial for your knowledge base to function effectively. Mainly, there are ML and NLP you can rely on, but this selection also depends on the context. The basic models may sometimes work due to more straightforward scenarios and use.
However, you can also use complex machine-learning models for more complicated scenarios. You can also rely on the platforms with pre-built AI capabilities for knowledge bases.
Once you have also decided on the AI model you must choose, it's time to train the model using the data you gathered earlier. For NLP models, you will be relying more on text data to train them so they can understand language patterns and context.
On the other hand, for ML models, you need to train them on historical data so it will help you predict outcomes or give recommended solutions. As new data becomes available, you need to update the model and retrain it to avoid any inefficiencies and keep it up to date.
Testing is an essential step once your model is trained. You cannot directly test it with users, so it is crucial to perform A/B testing, where you can compare the model's performance with agents or other knowledge-based systems.
You also need to test the model in different scenarios for various queries and edge cases to identify the quality of responses or any inconsistencies in the response behavior. If you identify any errors and issues, make necessary adjustments.
An essential part of building an AI knowledge base is continuously monitoring the performance. You need to see how it performs constantly and if the model meets customer requirements. You must also monitor the response rate and accuracy using various analytical tools.
Apart from this monitoring, you must also incorporate user feedback and see if users complain about the performance in any way. If users have a complaint, you should immediately incorporate that feedback and enhance the knowledge base accordingly. Continuously improving the knowledge base will lead to an enhanced customer experience.
The benefits of AI-powered knowledge-based systems are highlighted below.
The challenges of a knowledge-based system are as follows.
The following are the kinds of knowledge-based systems.
Case-based systems, as the name shows, rely on case-based reasoning. They use the past data and cases to solve new problems. The systems store and organize information based on past cases and then use the relevant information to solve a similar situation in the future.
The process in case-based systems starts with storing detailed information from a case, retrieving it when needed, adapting to the solutions from these cases, and finally giving recommendations based on this adapted information. This historical data helps in solving repetitive problems.
Expert systems are designed to solve problems as experts are solving them, helping people make essential decisions by combining domain knowledge with inference engine processes and then providing intelligent solutions.
It captures structured knowledge content and then uses logical reading to derive conclusions or make recommendations. Expert systems provide transparent explanations for the users so that they can also understand the reasoning behind those recommendations.
The system stores information in a network of interconnected hypertext blocks. These blocks are interlinked and lead to other blocks for more straightforward navigation for users, enabling them to access diverse information.
The system can create meaningful connections between information and provides a user-friendly interface to help users navigate the system quickly. It can also add new information and dynamic updates to the hypertext structure.
Intelligent tutoring systems are designed to provide personalized experiences by adapting to the needs of individual learners. The learning outcomes in this system are enhanced because it relies on cognitive psychology and educational theory.
The key features of ITS include adaptive learning paths, which are tailored to learning activities based on learner performance. It also can encounter feedback to enhance the learning process. It tracks learner's progress, assesses performance, and adjusts instructional strategies.
The rule-based systems operate on predefined rules that include decision-making and problem-solving. The rules typically follow an if-then structure where particular conditions trigger actions and conclusions. The collection of rules is in a set format.
It has an inference engine mechanism to apply rules to data and derive logical conclusions. These rule-based systems are used in various applications for business process automation, decision support, and expert systems.
The following pointers cover the uses of knowledge-based systems.
In collaboration with AI, knowledge-based systems can deliver results that distinctly help users with various problems. The two main types of AI, conversational AI, and Generative AI, are discussed below.
Conversational AI facilitates human interactions by using AI to generate human language inputs and enable interactive conversations. It also uses machine learning to learn from interactions and improve the quality of responses.
It also detects the context and intent, which leads to more accurate and relevant information and responses. It has various business implications, such as customer service, enhancing customer experience, and operational efficiency.
On the other hand, Generative AI generates new content that can be images, text, music, design, and other patterns. It also uses machine learning algorithms to create new personalized content for individual users and aids in creating marketing content.
The creativity of generative AI makes its implications very diverse in content creation and design innovation, which offers new ideas based on existing data.
The key differences between both are highlighted as follows.
Almost all the platforms have AI-powered knowledge bases in today's world of technology. However, we have compiled a list of top platforms with an AI-powered knowledge base.
As you can see from the list above, it's no longer Intercom that owns this market alone. There are plenty of cheaper, faster and less bloated alternatives.
AI knowledge base benefits businesses in various ways, but enhancing customer service stays on the top. If a business has satisfied customers, it eventually leads to other good things.
These AI-powered knowledge bases help businesses reduce customer response time, provide personalized recommendations, automate reactive tasks, give data-powered insights, and improve search and retrieval.
All these services offered by Artificial Intelligence to a business directly influence the type of experience your user has. This enhanced customer service across various steps leads to a seamless customer experience.
To sum it up, the AI knowledge base provides users an efficient way to interact with customers, especially expert systems, by retrieving information wherever needed. In short, it does everything a business does in no time, leading to increased team productivity.
