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. 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. Of course, this also has an impact on the implementation and introduction process of LLM chatbots. There are new requirements as early as the use case finding stage, which mean that the LLM chatbot implementation process has to be redefined in some places. In the following article, I will guide you step by step through the planning, implementation and publication of an LLM chatbot.
To repeat
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.
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.
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.
LLM chatbot: This is what a project workflow looks like
More and more LLM chatbots have been coming onto the market since the end of 2022. Of course, every LLM chatbot project is unique, individual and requires different resources and priorities. In general, however, companies go through the following steps during the planning, realization and implementation of an LLM chatbot.
The scope of the individual phases can vary depending on the company’s budget and objectives.
1. clarify requirements and define use case
The initial step in the planning of an LLM chatbot has essentially not changed. Every project begins with a careful needs analysis and the identification of a suitable use case.
However, the fact that LLM chatbots can be implemented much more efficiently than previous intent-based chatbots changes the cost-benefit analysis with regard to the question: “Does a chatbot make sense for our company?”. More and more companies are now able to implement their first LLM chatbot with little effort.
The possibilities of LLMs and RAG technology have also brought about significant changes in terms of applications. Whereas previously there was a tendency to define smaller use cases for limited subject areas, LLM chatbots with the corresponding data enable a large number of subject areas to be processed simultaneously. The limitations of a use case only become relevant if third-party systems or other partners may need to be integrated in later process steps.
You might find inspiration for your use cases if you take a look at my collection of best practices for LLM chatbots.
2. define technical and content requirements
Once the use case of the LLM chatbot has been outlined, the second step is to precisely define the technical and content requirements.
The content requirements include defining the knowledge that the LLM chatbot should have, including the ability to answer certain queries and perform additional capabilities, such as performing further process steps like changing an address. The tonality of the LLM chatbot is also an important aspect to consider, including how strongly the LLM chatbot should take on its own personality and how this personality relates to the company and the user. Some LLM chatbots heavily customize their personality to that of the user, while others are more aligned with the tonality of the company and some have almost no personality or tonality.
With regard to the technical requirements, the question of data protection is of crucial importance, including the location where the data used and stored by the LLM chatbot may be processed. Companies can choose between their own servers, servers in their own country or servers worldwide, for example in America. Other technical requirements include integration into other systems, such as CRM systems or internal ticket systems, as well as the decision as to where the chatbot is implemented, such as on a website, in an app or in a closed login area. Multilingualism is also an important aspect that needs to be taken into account.
It is crucial that this step is carried out carefully and in detail, as missing requirements can have a negative impact on the success of the project. It is therefore important to involve all relevant stakeholders in this project phase and take their needs into account.
3. select language model and technology
Once all requirements have been precisely defined, the technology and language model are selected. The decision for a specific language model is primarily based on the company’s data protection requirements. The higher the data protection requirements and the desire for own hosting, the more likely it is that an open source language model will be chosen.
With regard to the technology or technology partner, the first step is to check whether the company already has existing partnerships or contracts with technology providers that could potentially also take over the implementation of the LLM chatbot. Many companies maintain partnerships with Microsoft partners, for example, who could potentially also implement the planned LLM chatbot. (If a company does not have such partnerships, I am available to recommend a variety of trustworthy partners and will be happy to provide support).
It is possible that an official tender will be required as part of the technology selection process. This depends on both the company’s internal guidelines and the scope of the project.
4. collect and process data
As soon as the technology and the language model have been decided, the next step is to provide the data. Here, companies should work closely with the technology provider to ensure that the data is provided in a usable structure.
At the same time, companies are required to work very precisely here. If training data is forgotten or of poor quality, this will have a significant impact on the final quality of the LLM chatbot.
You can also read more about data provision in my article LLM chatbots – An introduction to the new world of bots.
5. create prompts
In addition to the data from which the LLM chatbot is supposed to learn, the prompts also play an important role with regard to the performance and behavior of the final chatbot. Companies can use prompts to define the behavior of the chatbot. This includes topics such as tonality, but also behavior in different situations. For example, companies can use prompts to specify how the LLM chatbot should deal with insults.
When creating prompts, the prompt engineer’s skills become apparent. Again, the more accurate and precise the prompts are, the more likely the LLM chatbot is to behave as it should. However, it must also be noted that despite good and tested prompts, the LLM chatbot will never be 100% under control. There can always be possible outliers.
6. implementation: Enrich and fine-tune the LLM with prompts and data
As soon as the data is ready and the prompts have been defined, implementation can begin. This process step is usually carried out by the technology partner. Of course, it is also possible for companies to work without an additional technology partner and connect to the language model themselves.
In general, if the data and prompts have been clearly defined and prepared in advance, the actual implementation should be relatively quick. The next steps will then require more resources and time.
7. testing and optimization
This phase shows the quality of the preparatory work. The LLM chatbot needs to be thoroughly tested and probably further optimized. Functional tests are generally unproblematic; however, content tests are much more critical and time-consuming. This involves evaluating how the LLM chatbot behaves in different scenarios and whether it provides the expected answers in an appropriate manner. If the results are not satisfactory, optimization measures must be defined. It is often necessary to adjust the prompts or add additional content. Training content may also need to be corrected or removed if it is misleading or incorrect.
This test phase is repeated until the LLM chatbot has reached a satisfactory quality.
8. integrate the chatbot into existing channels (chat or website)
Once the tests and optimizations have been completed, the LLM chatbot is ready to be integrated into the final chat channels. Most companies implement their LLM chatbot directly on their website or in their own app. This integration takes place during this phase.
9. publish the LLM chatbot
Once completed, the LLM chatbot is ready for publication. Companies can decide individually whether they prefer a soft launch or a launch with extensive public relations work. In the case of a soft launch, the LLM chatbot is usually quietly put online with little advertising. Other companies may even send out a press release to mark the occasion.
Regardless of whether a soft launch or a big launch is chosen, it is essential that companies inform their employees about the new LLM chatbot. This also includes detailed information on how the LLM chatbot works and its scope of services.
10. obtain feedback, optimize and further develop the LLM chatbots
Anyone who assumes that the last process step of an LLM project is publication is mistaken. The work continues after publication. The LLM chatbot must be continuously monitored and user feedback must be carefully reviewed and analyzed. In most projects, this phase leads to further optimizations and adjustments to the LLM chatbot despite previous test phases.
In addition to the optimizations that contribute to the improvement of the existing use case, the further development of the chatbot should also be considered after publication. It is likely that extensions to the use case or additional functionalities will be identified that the LLM chatbot could take on in the future.
LLM-Chatbot project: Frequently asked questions
How long does an LLM chatbot project take?
The duration of the LLM-Chatbot project depends on the scope of the use case. In principle, companies should allow at least two months or even more. Very fast companies can also do it in a month. However, this tends to be the exception
What does an LLM chatbot cost?
The costs of an LLM chatbot are usually manageable. Small projects can start with as little as EUR 10,000. However, it is important to bear in mind that the LLM chatbot also has operating costs that vary depending on the language model and technology partner. Additional resources are of course also required for further developments.
Are there also LLM voicebots?
LLM voicebots are like LLM chatbots, but they work with spoken language. The process of an LLM voicebot project is almost identical to that of the LLM chatbots.
How safe are LLM chatbots?
The security of LLM chatbots depends on the selected language model and the technology provider. In principle, LLM chatbots can be implemented very securely. There are already some LLM chatbots in the financial sector. These would not exist if security was not guaranteed.
How do customers react to LLM chatbots?
Initial evaluations of LLM chatbots show very positive reactions from customers with regard to LLM chatbots. Customers learn that the new technologies from LLMs and RAG have had a positive impact on the quality of chatbots. Customers are using the LLM chatbots more and more and the complexity of the requests is increasing.
Where can I see examples of LLM chatbots?
In my contribution to the collection of best practices of LLM chatbots you will find an overview of various LLM chatbots sorted by industry.
And when do you start?
Would you like to start your own LLM chatbot project? Or do you have any other questions first?
In both cases you are very welcome to contact me. I have already accompanied and managed many LLM-Chatbot projects and am very happy to be part of your project as well.
Just send me a message – preferably via WhatsApp message or 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.
*I used SwissGPT’s AI technology to optimize the language of this article.
