In our previous article, we saw the different terminology used to address chatbots mostly based on the objective they are fullfilling. Today we’ll see the differences between transactional (predefined workflows) and generative (using generative AI) chatbots.
| Transactional Chatbot | Generative Chatbot |
| A scripted agent uses predefined conversation workflows and responses based on rules and scripts that are manually constructed. | A generative agent uses generative AI, large language models (LLMs), and natural language processing (NLP) to understand, reason, and respond to customer queries in real-time. |
| Typically less expensive to develop and maintain since they follow predefined conversation workflows. Platforms offering KB generation facilities could be expensive. | Generative agents require more computational power and resources, which can make them more expensive to maintain and run. Cost of input and output tokens could be significant. |
| Scripted agents need more domain specific human intervention to define the workflows, intents, responses and scripts. | Generative agents require comparatively lesser human intervention while defining the capabilities of the system. |
| Limited to answering known questions and cannot adapt to new situations or conversations outside their programmed scope. The advantage of this is that these do not hallucinate. | Can learn and improve over time, generating contextual responses to new inputs. They are capable of handling complex requests without additional work. They can hallucinate hence guardrails are necessary. |
| Performance measurement in terms of accuracy is comparatively easier as the responses are proctored. | Measuring accuracy is complex, and responses need to be checked and validated not only for accuracy but also hallucination, bias, discrimination etc. |
| Their performance and capability depends on the underlying platform being used and the features it offers. | Their performance and capabilities depend on the underlying Large Language Model. Models may be fine tuned or Retreival Augmented Generation can be implemented. |
| They are more useful when you want to target specific workflows which are well defined. | They are beneficial when you have data locked in silos which are not accessible. Also, in cases where the workflows are not well defined. |
In conclusion, while transactional chatbots excel at handling specific, structured tasks, and generative chatbots shine in dynamic, open-ended conversations, the true power lies in combining both approaches. Our solution, Beacon Superbot seamlessly integrates transactional and generative features, delivering a versatile chatbot experience tailored to your business needs. Whether it’s managing routine tasks with precision or engaging users with personalized interactions, Beacon Superbot adapts to provide the most effective solution, ensuring your chatbot is not only efficient but also engaging and responsive to customer needs.
To schedule a demo – https://calendly.com/beaconcross/free-ai-consult