Search in a new way
A practical example of this is a house search application that we are currently making for a client. Searching through a website meant that the user generally searched by region and then used predefined filters such as price and number of square meters.
The LLM allows the user to start the search in any way:
The LLM interprets the input, can ask follow-up questions and then displays the matching results. This provides considerable added value compared to the usual way of searching for houses.
The LLM that we use here does not need to be built or programmed: as a technology, it immediately understands the input that users give. It is only a matter of providing relevant data and then fine-tuning where necessary. The LLM can be deployed very quickly.
In the example of a house search (or any other search application), an LLM offers the following new capabilities:
Conversational
Search Users can search for homes using their own choice of words by starting their search with questions and examples, such as "I'm looking for a house with at least 3 bedrooms near a train station" or "Show me houses with large gardens."
Flexible searches
Users can ask simple and complex questions, such as "Show houses with a swimming pool and a garage in Amsterdam " or "I want to rent an apartment in Rotterdam." They can use their own combinations of criteria, such as the number of bedrooms, the location, the type of home, the price range, etc. to obtain personalised results.
Understanding
Context LLM can interpret the context of the searches and ask relevant follow-up questions to clarify specific user needs, such as "What is your maximum budget?" or "Do you have a preference for a particular neighbourhood?"
Real time adjustments
LLM can be linked to an external system so that houses that have been sold and new supply are processed in real time.
2. Making data clear and accessible with language
Alongside interpreting language, LLMs also understand large amounts of data. For another customer with a wide range of products and services, we use an LLM to help users find answers quickly.
Previously, users were forced to choose a suitable channel for their question: via a chatbot, the navigation menu, searching by keywords, social media or else calling and selection menus. Then they had to interpret the information themselves. An LLM provides a single interface that can then point to the correct system or give the correct answer directly:
Answering User Questions
An LLM allows users to ask questions in everyday language about a range of products and services, such as "Which insurance is best for my car?" or "What should I do if I have stone chips on my windscreen?". The LLM understands the context which helps users faster.
Summarising Information
An LLM can extract and enumerate specific passages related to customer demand from sources. A next step is to give an unambiguous answer based on those passages. This keeps the information relevant and also allows the passages to be referred to as a source citation.
Understanding feedback
LLMs also have some quite serious limitations. You can combat this by combining an LLM with vectorisation. This allows you to trace how the system arrives at a specific answer and identify where this information comes from that creates nodes to help solve this. For example, you might find a page that hasn't been updated for two years or discover that the model needs additional training on this topic.
Personal assistance
An LLM can make personalised recommendations based on user preferences and previous interactions, for example, by saying, "We have an offer that suits you perfectly based on your previous holiday destinations."
Accessibility
An LLM can provide an inclusive and accessible way for users to ask questions, regardless of their experience with technology. For example, legal texts such as conditions can still be accessible to people with low literacy, in FAQs and documentation. This can be carried out using chat or voice.
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