As technology improves, it must also quickly evolve and adapt to the trending needs of businesses and consumers. The new craze is the creation and deployment of LLMs (Large Language Models). As with every new technology, there are drawbacks. Some more worrying than others, one in particular when the LLMs “hallucinate” and provide you with wrong answers. This could prove detrimental if you’re using LLMs to develop tools for customer support and they “hallucinate” in the response.
One of the more recent solutions is the concept of Retrieval Augmented Generation (RAG) added into the LLM used in the answer engine for customer support agents. It has provided a quick, and cheap solution to fix the LLM hallucinations.
For customer support, keywords are very important to detect and solve problems. AptEdge’s GPT Answer Engine provides a solution to prevent what you may experience with a chatbot trained by the GPT transformer from hallucinating. It follows a series of actions once added to the transformer model as seen below.
RAG is the concept of indexing keywords (with semantic context) into vector databases for faster querying response time. It takes these keywords from the knowledge base from which the LLM is trained and based on keyword matches with the concept of NLP (Natural Language Processing) the keyword is matched from the vector database and then all the information related to the keyword is fed to the LLM which, in turn, will impact the response of the query provided by the customer.
AptEdge utilizes RAG-based technology in a number of ways - book a demo to see for youself. Some key features are listed below as well.
With RAG, your support team can scale with an international market, without additional headcount. Leveraging LLMs with RAG, AptEdge’s AnswerGPT, allows them to handle more cases without the burden of constantly searching for answers themselves.
With AptEdge’s AI-driven search, RAG can dig into the query, then analyze the semantic context and provide appropriate responses to the problems that the customer queries to your agent, then AnswerGPT™ suggests an answer.
With the power of LLMs and access to historical data, RAG helps create personalized conversations with users and provide them with the best version of customer support by catering to them, specifically. This improves customer support and customer loyalty to your brand.
RAG empowered solutions are the future of customer support and the most impactful way to prevent LLMs from hallucinating. An LLM hallucinates because:
It doesn’t properly understand the question.
Its data isn’t updated frequently.
It has little to no critical thinking
The semantic context of a query is very important when it comes to customer support. In short, RAG involves indexing all relevant keywords available in a knowledge base.
Each word is taken from the knowledge base and indexed in alphabetical order in vector databases, which makes the search time for queries faster as compared to other traditional databases. There are already vector databases available from DataStax.
These are relatively low-cost methods and are not computationally complex. Due to this, many companies are opting to integrate Retrieval Augmented Generation (RAG) into their LLM models not only for customer support but for other tasks such as tagging types of problems, and automating developing dashboards visualizing data to the employees who can make business decisions to maximize profits.
This is using the vanilla LLM to develop answers. Initially, when ChatGPT came out, it was developed on GPT (Generative Pre-trained Transformer). It created ripples in the internet since it was new. After a while, ChatGPT users noticed that when ChatGPT or any chatbot trained using Generative AI can provide wrong answers if they aren’t aware of a query. Most, if not all chatbots were unable to grasp the semantic context of the queries asked by users.
Answers are generated based on the concept of combining iterative querying and RAG. For more understanding, see the diagram below.
Based on the responses obtained from iterative querying (asking the same queries in different contexts) and RAG-based responses from the vector database, multiple responses are generated. The LLM then takes all these responses and, by analyzing past chats, tries to select the most optimal response and responds to the query.
What does this mean to your customer support agent? Accurate and fast answers to customer queries, written in natural language.
With the quickly evolving technology of today, many people want accurate answers instantly. This has placed pressure on customer support deliver increased response times. GenAI provide a way to increase speed, but lacked semantic understanding. With the introduction of RAG paired with GenAI, your customer support team can become super agents and save the day!
You can be one of the businesses providing the best customer support with RAG-based chatbots by partnering with AptEdge.
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