Before diving into UltimateGPT, here's a quick glossary of some AI-related terms that will be used in this article.
- Natural language processing (NLP) - A branch of AI that allows computers to understand human language. It's a critical component of chatbots, used to interpret the user's queries using techniques such as named entity recognition, and sentiment analysis.
- Generative AI (gen AI) - A type of AI that can generate a wide variety of outputs including images, videos, audio, text, and more.
- Large language model (LLM) - A type of model in generative AI that is designed to generate human-like language based on statistical patterns learned from vast amounts of text data.
- Generative pre-trained transformers (GPT) - These are some of the most well-known LLMs popularized by OpenAI. They’re capable of a wide range of applications, including generating, summarizing, and translating texts.
Introduction to UltimateGPT
Introducing UltimateGPT, the LLM-powered bot that plugs directly into your help center to build a bot in minutes, instantly and accurately respond to your customers, with no training required.
LLMs are a type of artificial intelligence technology that is used to understand and generate natural language, such as the way humans speak or write. Many call this Generative AI.
They are trained on vast amounts of textual data (most of the internet) to learn the relationship between, and context of, words, sentences, and concepts. This allows them to perform many different CS tasks such as: having a conversation, summarizing articles, understanding the sentiment of conversations, etc.
GPT-3 (Generative Pretrained Transformer 3) and GPT-4 are examples of an LLM. They are language-processing AI models developed by OpenAI. They can generate human-like text and have a wide range of applications, including language translation, language modeling, and generating text for applications such as chatbots.
What does this mean for Ultimate?
LLMs have many benefits for the world of CS automation, but the main headline is that automated conversations become more human-like, customer experience is improved, and operation and maintenance are simpler and cost less.
This doesn't mean we are abandoning our bread and butter of NLP (natural language processing) - they serve very different functions. While NLP and LLMs are both language processing tools and an LLM can perform a variety of NLP tasks. LLMs are pre-trained on large amounts of general knowledge and use it to produce text.
Whereas NLP is trained on specific knowledge and makes the chatbot a subject matter expert, which only provides an answer when it fully understands the context.
This means for FAQs and content with straightforward answers with little-to-no processing or logic, LLMs are great. For structured replies that require logic, precise text, and following a process, NLP would be the solution.
Connect your help center to UltimateGPT to create a custom bot in minutes.
How it works in our platform is whenever there is a message that isn't covered via our NLP model, AKA our Intent structure, it is routed to the LLM to search and analyze your help center to find the correct answer, summarize it, and respond to your customer.
The great thing is that due to its contextual nature, it will always base answers based on the conversation that has happened before not just based on the last message so responses are impactful for the visitor.
Additionally, it will only work in the domain that you provide it as a framework (your connected help center), so it can't provide answers to topics it doesn't have access to - such as the internet.
Therefore, it will politely advise if it wasn't able to provide an answer and prompt the user for elaboration or clarity.
Whenever it encounters small talk or topics outside of the domain, the answer is programmed to always pivot back to what it is trained to respond on.