My AI assistant has also created a podcast episode for the following article. If you prefer listening to reading, you can listen to the podcast via the following link (ATTENTION: Podcast is created exclusively by AI, no guarantee for accuracy).

Chatbots and voicebots have undergone a significant change in terms of quality and implementation methods through the integration of Large Language Models (LLMs). In the past, companies had to laboriously predefine every question and answer, which made the development process lengthy and inflexible. On the user side, too, the experience was usually rather sobering, as the predefined answers were rather general and not very user-centered. Today, LLMs and methods such as Retriever Augmented Generation (RAG) make it possible to train chatbots and voicebots quickly and efficiently so that they are able to communicate in a very specific and target group-oriented way. This initially facilitates development and implementation, as the bots can now respond dynamically to a wide range of requests without having to program every possible conversation scenario in advance. At the same time, it has taken the customer experience to a whole new level. The quality of the answers has increased many times over in terms of correctness and individuality.

Here’s everything you need to know about this new type of chatbot.

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What are Large Language Models (LLMs)?

A Large Language Model (LLM) is an advanced machine learning model that specializes in understanding and generating human language. These models, which are based on deep neural network architectures such as the so-called transformers, are trained with gigantic amounts of text data. Through this training, they learn to recognize patterns, structures and the meanings behind words and sentences, which enables them to communicate in natural language. LLMs can be used for a variety of applications including text generation, translation, summarization and question answering by effectively generating new text based on learned context and input prompts.

What does Retriever Augmented Generation (RAG) mean?

Retriever Augmented Generation (RAG) is a method in natural language processing that combines an information retriever and a text generator to generate precise answers to user questions. The process begins with the retriever retrieving relevant documents or data from an extensive database based on the match with the user’s question. This selected information is then passed to the generator, typically an advanced language model such as a Transformer-based Large Language Model. The generator uses this information to formulate a coherent and informed response. This method makes it possible to generate answers that are not only based on previously trained knowledge, but also include current, specific and context-related information, which significantly improves the accuracy and relevance of the answers.

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What is an LLM chatbot?

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.

How does an LLM chatbot work?

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

1. the brain

The brain is the fundamental part of an LLM chatbot and acts as the central processing unit or the “brain”. As with humans, the brain manages the bot’s overall logic and behavioral characteristics. It interprets user input, applies logical conclusions and determines the most appropriate course of action based on the chatbot’s capabilities and goals defined by the company. The brain ensures that the bot acts correctly and consistently according to predefined guidelines or learned behavior patterns.

2. the memory

The memory serves as storage for the chatbot’s internal logs and user interactions. This is where data is stored, organized and retrieved. This enables the bot to remember previous conversations, user preferences and contextual information and thus provide personalized and relevant answers. Memory is crucial as it provides a time frame and stores additional details that are relevant to specific users or tasks. Companies can decide for themselves where the brain stores the data and thus ensure that their own data protection requirements are taken into account by the LLM chatbot.

3. workflows

Workflows are predefined processes or tasks that the chatbot should be able to perform. These workflows can range from answering complex queries and coding to searching for information and performing other specialized tasks. They are similar to the various applications and utilities in a computer that enable a wide range of functions. Each workflow is designed for a specific purpose, and the brain intelligently decides which tool to use depending on the context and type of task. This modular approach allows companies a high degree of flexibility and scalability, as new workflows can be added or existing ones updated without compromising the overall functionality of the chatbot. The chatbots and voicebots can thus easily learn new skills and functions.

4. the planning module

The planning module is the component that enables the chatbot’s ability to tackle complex problems and refine execution plans. It is comparable to a strategic layer over the brain and workflows that allows the LLM chatbot to not only respond to immediate requests, but also to plan long-term goals or more complicated tasks. The planning module evaluates different approaches, anticipates potential challenges and develops strategies to achieve the desired result. This can be, for example, the overarching goal of “more sales”.

5. prompts

We are most familiar with prompts through the use of ChatGPT or similar technologies. LLM chatbots also work with prompts. Thanks to prompts, companies can define the behavior of the chatbot and prevent unwanted reactions from the bot as far as possible. A distinction is made between two main types of prompts:

General prompt:

• This prompt outlines the capabilities and behavior of the bot and forms the basis for the interaction and reaction of the agent. It acts as a high-level guide that shapes the entire functioning of the agent.

Task-related prompt:

• This prompt defines the specific goal that the LLM chatbot must achieve and guides its actions and decision-making processes. It ensures that the chatbot’s responses are tailored to the task at hand, whether it’s answering a customer query or carrying out a complex analysis.

How do companies implement LLM chatbots?

The implementation of LLM chatbots comprises the following seven steps:

• Data collection
• Data preprocessing
• Training the language model
• Fine tuning
• Testing and optimizations
• Provision and integration
• Continuous learning and improvement

First, a comprehensive and company-relevant collection of content is compiled, which serves as the basis for the language model training. The collected data is then cleansed and tokenized to prepare it for training.

In the training phase, machine learning methods, in particular NLP strategies, are used to train the model on the cleansed data set. This is followed by fine-tuning for specific applications in order to increase accuracy for certain tasks. After the initial testing of the LLM chatbot, in which areas for improvement are identified, iterative refinement follows by adjusting the training data and other model parameters.

As soon as satisfactory performance is achieved, the LLM chatbot is implemented in the company’s target environment and integrated into existing systems via APIs. To ensure that the chatbot is up-to-date and relevant, it is regularly retrained with new data and continuously improved through feedback loops. These steps ensure that the LLM chatbot provides accurate and relevant answers that meet current user requirements.

What applications are there for LLM chatbots?

LLM chatbots are already very versatile and can be used in numerous areas. Here are some of the most important applications:

1. Customer service: LLM chatbots are often used in customer service to answer frequently asked questions, manage support tickets and offer solutions to problems. They can be available 24/7 and thus significantly reduce waiting times for customers.
2. Personalization of marketing campaigns: LLM chatbots can send personalized messages based on customers’ preferences and previous behavior. They can also help with conducting surveys to gather better customer feedback.
3. E-commerce and retail: In online stores, LLM chatbots can help customers select products, make product recommendations and support the purchasing process.
4. Healthcare: In the medical field, LLM chatbots can provide patients with information on symptoms, support initial pre-diagnoses and offer advice on taking medication. They also serve as the first point of contact for assessing the urgency of cases and allocating resources accordingly.
5. Financial services: In the financial industry, LLM chatbots help automate queries about account balances, transactions and can provide advice on basic financial matters.
6. Education and training: LLM chatbots can act as interactive learning assistants, providing learning materials, conducting quizzes and responding to specific questions from students.
7. HR and recruitment: LLM chatbots can support the recruitment process by screening CVs, conducting initial interviews and automating communication with applicants.
8. Internal business processes: LLM chatbots can also be used internally to give employees quick access to company information, facilitate administrative tasks such as booking rooms or managing calendars.

What are the advantages of LLM chatbots?

LLM chatbots offer companies and end users a wide range of benefits. Below are some general advantages of chatbots, followed by specific advantages of LLM chatbots compared to chatbots without LLMs:

General advantages of chatbots:

1. Availability: Chatbots are available around the clock and can answer user queries without interruption, which is particularly valuable outside of business hours.
2. Scalability: Bots can handle thousands of requests simultaneously, making them ideal for large companies or events with high user traffic.
3. Cost efficiency: Chatbots reduce the need for human staff and can therefore significantly reduce the costs of customer support and care.
4. Consistency: Bots provide a consistent quality of answers and user experience, which contributes to brand consistency.
5. Data collection: Chatbots can collect valuable data about user interactions that can be analyzed to improve products, services and customer experiences.

Advantages of LLM chatbots over traditional chatbots:

1. Improved understanding: LLM chatbots based on Large Language Models such as GPT have a deeper understanding of language, enabling them to provide more natural and contextually relevant responses. You can better understand and respond to complex requests.
2. Adaptability: Thanks to their trained understanding of language and context, LLM chatbots can adapt more quickly to new topics and requests without having to be explicitly programmed for each new requirement. This makes the development and customization process for bots much easier and faster.
3. Personalization: With advanced language understanding, LLM chatbots can provide more personalized interactions by taking into account tone, mood and previous interactions to make communication more individual. This increases the customer experience enormously.
4. Ability to generate long text: Unlike older models, which could usually only generate short and simple texts, LLM chatbots are able to create more detailed and informative content, making them useful for applications such as content creation, detailed product descriptions and educational purposes.
5. Integration of external knowledge: LLM chatbots, especially those using RAG, can tap into company-specific data sources to inform and improve their responses. This enables them to provide up-to-date, accurate and in-depth information that is a perfect fit for the company.

Who are LLM chatbots suitable for?

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.

When is it worth using LLM chatbots?

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.

Are there still chatbots without LLMs?

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.

What do companies need to consider when introducing and using LLM chatbots?

As described above, the realization and implementation of LLM chatbots is relatively simple and structured. However, special attention should be paid to the following points.

1. Data protection: It must be ensured where the LLM chatbot stores its data and this storage location must be in line with the company’s compliance rules.
2. Data sources: Companies must have clean and relevant data sources for the LLM chatbot. Many companies use their own website as a basis. As long as it is properly managed, this does not present any challenges. However, if the website contains outdated data, companies must first clean it up.
3. Employee training: The role of employees must not be ignored. Companies must provide their employees with sufficient training and also explain the background to the LLM chatbot to them.
4. User experience: Due to the use of LLMs, many chatbots tend to give very long and detailed answers. Companies need to find a good balance between the depth of the response and the scope of the response. This may vary depending on the request.

What are the risks of LLM chatbots?

1. Quality problems: Even if LLM chatbots are generally given fixed rules for behavior in advance and companies can also limit the training knowledge of the chatbot, incorrect answers can occur in rare cases. This cannot be completely ruled out, but it is constantly being improved.
2. Lack of control: LLM chatbots generate a new response for every user request. Companies have no direct control over the chatbot at this point. This makes it all the more important that the bot is sufficiently tested before it is published.
3. Data protection and security: LLM chatbots store conversation data and other information. It must be ensured that no data is disclosed to third parties without being asked and that the type of data storage is in line with the company’s compliance requirements.

How do customers react to LLM chatbots?

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.

What are the best practices for LLM chatbots?

The LLM chatbot at Helvetia Insurance

The first LLM chatbot from Switzerland is called Clara and is from Helvetia Insurance. The LLM chatbot initially uses the information on the website to answer customers’ and potential customers’ questions about insurance. In further iterations, the insurance company has supplemented its knowledge and expertise with additional internal connections. Read more about the LLM chatbot at Helvetia in my interview with Florian Nägele on LLM chatbots in the insurance industry.

The LLM chatbot at the retail hobby market Jumbo

The Swiss DIY and hobby store Jumbo has been publishing an LLM chatbot to advise website visitors for over a year now. The bot acts as a product advisor and is available via the website. Customers can ask their questions about product details or product recommendations and the chatbot responds based on its own knowledge base. The knowledge base was compiled by the Jumbo-Digital team and roughly contains the website content as well as further product detail documents. Read more about the JumBot chatbot in my article on LLM bots in retail.

You can find more examples of LLM chatbots in my article on the best practices of LLM chatbots.

Conclusion: LLM chatbots have changed the chat and voicebot world

When I wrote my master’s thesis on chatbots almost 10 years ago, I had to conduct the majority of my experiments with rule-based chatbots. I often only used mockups, as even the implementation of simple chatbots was very time-consuming. LLM chatbots have changed the world of bots and will continue to do so. The realization and implementation is significantly simplified by the methods of LLMs and RAGs and the quality has already increased many times over. In the long term, an LLM chatbot on the website or internally at the company will probably become a commodity and be just as natural as the company’s own website.

And now?

If you would like to know more about LLM chatbots or even gain your own initial experience, please send me a message. You can send your message by WhatsApp message or by e-mail.

This article is also available as a podcast episode

Attention! The podcast was created entirely by my AI-Assistant based on my contribution – no guarantee for incorrect content.