The knowledge base in LLMs is directly proportional to the quality of user experience a company can offer.
The Knowledge Base in LLMs: How the Future of Customer Experience Is Evolving
In a digital landscape increasingly shaped by generative artificial intelligence, building and managing an effective Knowledge Base (KB) is becoming a strategic imperative for companies of all sizes. This was the central theme of the webinar “Knowledge Base in the Era of LLMs,” hosted by Eleonora alongside Stefano Somenzi, CTO of Athics, who provided a clear and practical overview of the KB’s pivotal role in an AI-driven ecosystem.
Why the Knowledge Base Is Crucial Today
As Stefano highlighted, the quality of a company’s Knowledge Base directly impacts the quality of the user experience it can deliver. A well-structured, consistent, and comprehensive KB enables chatbots and conversational systems powered by LLMs (Large Language Models) to provide relevant, personalized, and reliable responses. In short, it forms the foundation of truly effective customer support.
But what does a “well-designed” KB really mean? According to Somenzi, there are three key pillars:
- Consistency: Avoiding contradictory or ambiguous content;
- Completeness: Thorough coverage of the relevant domain;
- Cost-effectiveness: Sustainable management and updating, supported by Generative AI.
What Generative AI Can (and Can’t) Do
Generative AI isn’t a silver bullet. Certain use cases — such as risk prediction or logistical planning — still lie beyond its optimal scope. However, when it comes to content generation, natural conversational interfaces, or data extraction and archiving, generative models can make a significant difference.
In particular, they can dramatically accelerate the creation of complete KBs, simplify human-machine interfaces, and reduce operational costs. But a word of caution: human oversight remains essential to ensure that what is generated is both coherent and accurate.
From E-commerce to Marketing: Real-World Use Cases
Generative AI is already being applied across industries in a wide variety of ways:
- Customer support: Automated responses, feedback collection, and pattern recognition in complaints;
- Marketing: Personalized messaging, faster A/B testing, and natural language data analysis;
- Decision-making: Predictive analytics and business diagnostics through conversational data queries.
First Steps Toward Building an Effective Chatbot
For those looking to implement an advanced chatbot, Stefano recommends starting with a fundamental question: What kind of data are we feeding the AI? A well-organized KB — free from ambiguity, consistent, and regularly updated — is essential. And that’s where supervision becomes critical: AI is an accelerator, not a replacement for human critical thinking.
Looking Ahead: Privacy, Security, and Emotional AI
Looking to the future, Somenzi sees growing focus in two key areas: privacy and security (with tools to ensure compliance and data protection), and the development of Emotional AI — the ability of systems to understand users’ psychometric traits, not just their immediate emotional state.
Finally, one crucial point: true multimodality. AI systems will no longer be limited to text or images, but will be able to process and integrate video, audio, images, and structured data into a single, much richer and more immersive user experience.