Is it true that a major limitation of older types of bots was that updating their knowledge base was both slow and expensive?

Updating the knowledge base of older types of bots, such as rule-based or scripted bots, often involved a complex process. These bots typically relied on fixed algorithms and predefined rules that dictated their responses. As a result, when there was a need to improve their performance or adapt to new information, it required manual intervention, which could be time-consuming and labor-intensive. This meant that adjustments or expansions to their knowledge base were not only slow to implement but also incurred significant costs in terms of resources—both human and technological.

Additionally, since these systems were often designed without flexibility, integrating new data or learning from interactions was challenging. In contrast, modern AI-driven bots utilize machine learning and natural language processing, allowing them to learn from new data in a more dynamic and cost-effective manner. This advancement marks a significant improvement over the limitations associated with older bot systems.