Your conversational AI solution will only be as good as your knowledge base. Routinely, performance problems with conversational AI are traced back to the knowledge structure. A change of model is the often-sought solution to performance issues, but it’s not going to solve the underlying knowledge structure problem.
When it’s not the model
The best model in the world can’t fix poor knowledge management. To get the best returns from your conversational AI solution, a knowledge base structure first approach should be adopted.
Conversational AI systems are not stand-alone solutions. They operate within a broader ecosystem and are dependent on having access to a whole host of content, such as internal documentation, product information, operational procedures, policies, and company information.
The AI model can’t perform miracles, and can’t produce accurate, reliable answers if the knowledge it is accessing is outdated, inconsistent, disorganised, or not available. The effectiveness of the conversational AI solution is inextricably linked to the quality of information it can access.
If you want effective, accurate, reliable, and trustworthy conversational AI, pay attention to your knowledge base now.
Why the knowledge base is king
Conversational AI systems work by retrieving information. It is only with quality knowledge, that quality retrieval will take place and the AI response will be of a high quality. This quality is what builds user trust.
To work effectively conversational AI is reliant on clarity, consistency, and retrievability. The model cannot solve the problems in the knowledge base.
Loss of trust
Documentation that is duplicated, conflicting or inconsistently spread across multiple locations, with no single source of trust for the conversational system to use, results in inconsistency of responses, increased hallucinations, and failures in retrieval.
When documentation is outdated or procedures no longer valid, but they remain accessible, the conversational AI system can provide plausible answers that are incorrect. This causes users to lose trust quickly.
Unless the information is explicitly managed then the AI cannot differentiate between outdated and current information.
Inconsistency in terminology will also impact the user. If the same concept is named differently across documents, or acronyms are used inconsistently the knock on will be partial or incorrect answers, reduced accuracy, and retrieval failures.
Knowledge base structure and hierarchy that include long unstructured documents, an absence of headings or categorization, or topics that have no clear relationships increases ambiguity and impacts the precision of retrieval.
When quality at source is missing
There are several key indicators that will suggest the knowledge base is the cause of an under-performing conversational AI system:
- There is inconsistency in responses when similar questions are asked
- Correct answers are available but are not retrieved consistently
- Different answers are given to different departments
- AI is not working at a consistent level across all areas
- Simple questions are frequently being escalated to humans
Without quality at source businesses suffer from reduced AI effectiveness, loss of user trust and increased operational costs.
Improving the knowledge base has significant benefits
Our experience has shown that when attention is paid to the knowledge base, businesses see:
- A reduction in errors and support workload
- Improved human employee efficiency
- Improved AI performance
- Increased user trust and usage
- Acceptance of future automation
Focusing on the right things
The primary performance driver of conversational AI is the knowledge structure and is the foundational infrastructure for AI success. Upgrading the knowledge base will deliver significantly better improvements than upgrading the model.
Conversational AI systems rely on the information available to them. The quality of the information system determines the quality of the responses. Ensuring a single source of trust, regular document maintenance, standardization of terminology, and a clear document structure and hierarchy will result in substantial improvements in AI performance. The winners are customers, employees, and the business overall.
