What ChatGPT Means for Customer Support and the Role of Vector Databases
This article explains how chatbots and self-service platforms are becoming more popular in improving AI-based knowledge bases.
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Most organizations consider customer service an overhead while it is an opportunity. It allows you to drive continued value from the customer even after the sale. Successful businesses understand that customer service doesn't just help you retain customers but also gain more revenue. It is an underrated tool to enhance your marketing and sales efforts through referrals, testimonials, and classic word-of-mouth. However, it is critical to service customers in real-time without delays. With the emergence of AI, this requirement is rather achievable.
With AI, it is possible to support your customers across their journey by assisting them on the go. Also, the possibilities could be more realistic with AI-led chatbots and machine learning (ML) capabilities like NLP and instant data analysis. Finally, with the growing adoption of vector databases, businesses can tap into unstructured data to cater to their customers.
AI in Customer Support
Interestingly, the first-ever AI chatbot used in customer support was in the 1960s, when ELIZA, a psychologically intelligent virtual assistant, helped doctors with diagnosis and treatment. After that, it took a backseat. Until now, when customers demand instant gratification. 90% of customers expect an immediate response to their customer service question, according to Hubspot research. Further, the report also revealed that 80% of the customers stopped doing business with a company after a poor experience. This underscores the importance of good customer service and being available to your customers 24/7.
Nonetheless, this paradigm shift wouldn't have occurred more appropriately. With ChatGPT coming onto the global scene, we could witness a revolution in AI-led customer service.
The Rise of ChatGPT
ChatGPT is being hailed as a new turn in this information age with an AI-based platform that gives answers to complex questions in a conversational manner. Built by OpenAI, it was designed and trained to understand what humans mean when they submit a query. As a result, ChatGPT shattered the possibilities of conversational AI.
ChatGPT: A revolution in the age of information?
ChatGPT is an advanced chatbot built on GPT-3.5 that can converse with humans in a dialogue form. It follows something called the Large Language Model (LLM), which is trained to accurately predict the next word in a sentence. While it might seem like your auto-complete feature on your mobile, ChatGPT does it at an unrealistic scale. Researchers learned that the more data it is exposed to, the better its conversational capabilities improve.
Some of the business use cases of ChatGPT are listed below:
By training ChatGPT on a large set of customer interactions, you can automate response generation to the most frequently asked questions.
You can create social media posts or product descriptions by submitting proper queries.
You can monitor customer mood by analyzing sentiments from the feedback statements.
It can analyze patient data rapidly to suggest the right diagnoses and treatment plans (A more advanced form of ELIZA).
ChatGPT can generate messages, emails, or any content very easily.
GPT -4's Incredible Aspirations
While we are comprehending ChatGPT's capabilities, OpenAI has already introduced an upgrade to it in the form of GPT-4. While its predecessor has 175 billion parameters, GPT-4 will reportedly have 1 trillion parameters, making it incredibly faster and smarter.
For every query you pose, GPT-4 will process it with 1 trillion parameters to give the most accurate result. Although it is yet to be released, GPT-4 will cause a mind-bending transformation in customer service.
Challenges of ChatGPT for Customer Support
ChatGPT returns responses to the queries submitted based on the information it has been exposed to. Therefore, it will have limitations when you use the tool to service customers on your website without training it first. In addition, since it can only source information about your company from internet-facing assets like websites and other portals, the answers may not be accurate or helpful.
Limitations of ChatGPT in servicing customers.
The second limitation of ChatGPT is the inherent nature of customer queries. Most customer questions are vague and need logical translation to be able to provide an appropriate answer. ChatGPT, unfortunately, is yet to master that art.
ChatGPT may not be fully capable of managing your customer service yet, but that shouldn't stop you from applying AI to improve customer experience.
Build Your Own AI Customer Support Agent
Many organizations limit their AI strategy to improve customer service with an engine to generate automated and premeditated responses, which are mostly generic. The customers, however, expect personalized, more competent answers and faster delivery times. You can provide accurate and on-demand customer experience by building a CS agent that uses NLP (Natural Language Processing) and NLU (Natural Language Understanding) to understand the context of customer queries. Then, by infusing it with AI-run search capabilities, you can deliver seamless human-like virtual conversations.
The primary challenge with offering AI experiences is that companies possess a sea of unstructured data, which is complex to manage and analyze. That perception quickly changed with ChatGPT, although vector databases have been in use to manage unstructured data since before.
The below-shown architecture defines a seamless and effective customer support agent workflow:
Building an AI customer support agent.
The AI-based customer support flow involves two different flows – one with indexing service and the other with query service represented in green and yellow, respectively. Let's take a look at how they work.
An Indexing service transfers data to and from the knowledge base that contains documents. With every document added or changed in the knowledge base, Embedding's API is activated to convert the new information into vectors. These vectors are then added to the vector database to facilitate a quick semantic search.
Using a query service, you can provide a textual query, which is turned into a vector by the Embeddings API in a process that is similar to indexing. This vector is then used to search and match with documents through the database, and the best result is given out. Since the search engine already has vectors of documents ready, it makes the process easy and fast, even for millions of documents.
What Is a Vector Database?
A vector database is a fully managed way of storing, indexing, and searching across unstructured data through embeddings powered by ML (Machine Learning) models. It efficiently simplifies datasets by representing data objects into numerical values for easy management in a process known as vector embeddings.
Vector databases index these embeddings so that you can compare vectors with each other or to the vector of a search query. Vector databases facilitate data management functions like create, read, update, and delete. Similarity search and metadata filtering are two other essential features of vector databases to give you comprehensive search capabilities.
Some examples of vector databases are:
- Qdrant: A similarity search engine and vector database are available with an API providing services like storing, searching, and managing vectors, along with implementing dynamic query planning and payload data indexing. Qdrant is a powerful and scalable option among other vector search engines and is a great option.
- Vertex: Built by Google, Vertex AI Machine Engine is a low latency vector database that organizes vector embeddings based on their unique aspects to facilitate easy and scalable search.
- NucliaDB: NucliaDB is an open-source, cloud-native vector database and distributed search engine that allows you to store your data on its cloud infrastructure.
Language AI Services
The application of AI for language analysis is fast becoming a trend across industries. Various businesses are finding use cases for AI to decode text and derive valuable business insights. The text can be in written, spoken, or visual format. You can leverage your unstructured data: text, voice, images, and video - to generate AI datasets that can be used to smarten your ML algorithms and models.
Quite a few companies like OpenAI, Cohere, and AI2Labs offer APIs that allow you to access advanced models facilitating natural language applications.
Future of Customer Support
Customer service is expected to make giant leaps backed by emerging technologies improving customer experience and your ability to support a customer better. Companies are looking to rely heavily on self-service platforms and chatbots to improve their knowledge bases to hone AI-based conversations. In addition, the advancements in NLP in recent years have made virtual assistance a seamless customer service tool. For example, a chatbot can now conduct human-like conversations needing human interference only in the cases of complex scenarios.
Published at DZone with permission of Twain Taylor, DZone MVB. See the original article here.
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