Chat models are prepared in such a way as to be helpful to the user, therefore the generated answers may often contain false information due to the strong need to answer the question asked.
If you ever want to use a chatbot based on a language model, you may be interested in how such a model would know about the services or products you offer? Models such as GPT-4 or Llama 2 are already able to reason logically, but they certainly lack the knowledge that could help your customers in contacting your brand.
So how can chatbots based on Large Language Models (LLMs) operate on knowledge and data that is unique to your business during conversations?
There are many techniques that are being constantly developed, and one of them is the RAG technique ( retrieval-augmented generation ), which is a method of extracting information needed to provide an answer gcash database from an external source of knowledge (relative to the knowledge embedded in the language model), such as a database.
This technique is used by, among others, IBM's watsonx™ Assistant. It is one of the leading AI platforms that allows the use of large language models to provide an exceptional customer-chatbot experience. This and competing solutions often offer:
intuitive user interface,
automated self-service support across multiple channels,
creating voice agents,
integration with other platforms that support your activities.
The most important stage in the RAG technique is the stage of indexing information. During this stage, data structures are created that will enable later searching. In the case of language models, so-called embeddings will almost always be used, i.e. a numerical representation of knowledge that somehow stores the meaning of a given fragment of text. Texts with similar meaning will have embeddings that are similar to each other. Capturing the meaning of the text in the form of embeddings allows for easy searching of knowledge sets in terms of a given question.
Typically, some metadata is added to such prepared embeddings, which can later be used to provide the user with the sources based on which the model generated the response.
Based on the user's question and the conversation history, the chatbot can search the database to find information semantically related to the question, which will constitute additional context, which will then be processed by the language model along with the question.
Matched fragments from the knowledge base can then be attached to the context, which, together with the user's question, goes to the language model, thanks to which it will be able to logically answer questions, while taking into account the provided context (fragments of text from the knowledge base). Having a set of facts in the form of a specially prepared knowledge base significantly reduces the risk of hallucinations of the language model, which leads to significantly better quality of answers, which can translate into increased satisfaction with using the chatbot.
LLM-based Chatbots and Costs
Dynamically selected information from the database is supposed to solve the problem of lack of knowledge and it really works in most cases. Unfortunately, for now, implementing an allegedly simple solution and scaling it to thousands of users is still expensive, but there is a clear trend towards reducing the costs of using models (competition, as well as open source solutions), as well as a move towards smaller models specializing in specific tasks, so we can predict a general decrease in prices for using quality language models.
Summary
Chatbots are used in business, automating communication with the customer. They are used not only in marketing, but also as technical support, or can perform simple tasks in the context of customer service. Trends indicate that in the future there will be more solutions and applications of this type. It will certainly not be possible to ignore the potential benefits of implementing this type of system. The ability to generate personalized responses based on previously provided knowledge along with the ability to take certain specific actions will mean that in the near future we will see more and more helpful chatbots in the form of, for example, a virtual assistant in an e-commerce store.
