Chatbots are still somewhat negatively charged due to the poor quality of rule-based bots. However, this negative attitude is diminishing more and more. Numerous best practices, such as that of Helvetia Switzerland, show customers and companies that LLM chatbots have a significantly higher quality and response accuracy. Initial figures show that customers and users of chatbots are increasingly understanding this and the aversion to LLM chatbots is decreasing. Customers are becoming more and more motivated to start a chat with an LLM chatbot and are constantly gaining new and positive experiences.

The main task of LLM chatbots is the automated answering of questions or the triggering of predefined processes. Consequently, LLM chatbots are suitable for all companies where employees have to answer similar questions repeatedly during their work processes. It should be noted that LLM chatbots can be used internally for employees or externally for customers. This means that an IT helpdesk or an HR department that regularly receives queries from internal employees can also be relieved by an LLM chatbot. LLM chatbots for customers are mostly used in customer service or occasionally in marketing.

Due to the simplified implementation of LLM chatbots and their greatly improved quality compared to rule- or intent-based systems, the investment in LLM chatbots pays off earlier today than it did a few years ago. In general, the use of LLM chatbots is appropriate wherever the content for answering queries already exists in existing knowledge sources. Companies where this is the case and which receive a high number of inquiries every day should consider using an LLM chatbot.

Chatbots without the integration of an LLM are rarely implemented today. However, there are more and more hybrid forms. In many cases, these are chatbots that were initially developed without an LLM and without a RAG and are now being added later.

I personally find these mixed forms tricky. The chatbot then gives a mixture of fixed or predefined impersonal answers and, at the same time, LLM-generated answers that are much more specific and personal. This mixture often represents an interruption in the customer experience.

LLM chatbots, or Large Language Model chatbots, are advanced AI systems that use Generative AI to understand and generate human language. These intelligent chatbots are based on large language models such as GPT-4 or other open source models that have been trained with enormous amounts of text data to develop a deep understanding of context, syntax and semantics. This advanced language processing allows LLM chatbots to perform a variety of tasks, from answering questions and creating content to automating customer support.

Methods such as Retriever Augmented Generation (RAG) play an important role in connection with LLM chatbots. RAG combines the capabilities of a retrieval system, which retrieves relevant documents or information from a database, with the generation capability of a large language model. This enables LLM chatbots not only to respond based on the trained model, but also to integrate specific, contextual information from the company’s own sources in order to generate more precise and informed answers. The use of RAG therefore significantly extends the functionality of LLM chatbots by enabling companies to supplement the knowledge of the chatbot individually. Companies can even define that the LLM chatbots should only access the content provided by the company. This ensures that the bot does not access unwanted or incorrect information.

LLM chatbots consist of several main components. I like TrueBlue’s illustration, which breaks down LLM chatbots into a very simplified five main components:

Find out more in my article: LLM chatbots – an introduction to the new world of bots

Chatbots are digital chat robots that automate dialogs. Instead of chatting with a human, customers then chat with an artificial intelligence.

Chatbots offer customers and employees 24/7 availability. They can answer the most frequently asked questions around the clock and are infinitely scalable.

Chatbots increase the efficiency of employees. Employees can get back to dealing with demanding customer inquiries, while the chatbot or voicebot answers simple questions automatically. This increases customer and employee satisfaction.

Basically, my offer here is very broad. I provide support in all of the above points, but I can also take on individual points. Depending on the client, I either take over the project management and actively support the implementation or I simply accompany the project as an external consultant.

Of course, I always take the latest technical developments into account in my consulting. In my opinion, chatbots that use LLMs, generative AI and RAG are the future. Consequently, they are also an integral part of my services.

Yes, I have already supervised several Voicebot projects and would also be happy to accompany your Voicebot project.