Mike is an experienced Product Manager who focuses on all the “non-development” areas of My AskAI, from finance and customer success to product design, copywriting, testing and more.
Alex is an experienced CTO and founder who largely focuses on all the technical areas of My AskAI, from AI Engineering, Technical Product Management and overall Platform Development.
We’re now in thefourth industrial revolution—an age of bustling tech breakthroughs reshaping industries. At the core of this revolution is none other than artificial intelligence.
In 2023,AI went mainstreamand started altering our way of life. AI chatbots like ChatGPT, Bard, Claude, Gemini, and CoPilot can now engage in human-like conversation and assist us with absolute speed and accuracy. Behind these AI chatbot innovations are large language models (LLM), AI systems that comprehend and generate human language.
LLM chatbots are essential in various business operations, such as customer support. These chatbots can “talk” to customers, analyze their requests, gauge their sentiments, and assist in solving their problems, thereby improving overall customer satisfaction.
Learn more about LLM chatbots and their benefits to customer support in this article.
The Basics of Large Language Models (LLMs)
A language learning model (LLM) is a computational deep learning model trained using vast amounts of data to understand and generate human languages.
Upon training, LLMs are capable of providing answers to users’ prompts, summarizing textual passages, or engaging in conversational interactions.
LLMs use atransformer neural networkto operate. The model’s architecture somehow resembles the human brain as it uses nodes (just like brain neurons) in a layered structure to process textual inputs (which are sequences of words) and predict the outcome (which is another series of words that the algorithm generated).
There are multiple, deep layers involved in the architecture, thereby making LLM a “deep” learning model.
Meanwhile, the “large” in LLMs pertains to their massive parameter counts. Parameters are internal variables that the model learns to respond to prompts. For instance, GPT-3.5 has175 billion parameters, while GPT-4 has around 1.8 trillion parameters.
How do LLMs work?
The LLM’s response depends on its training data, which can include information from the internet, books, encyclopedias, and more. Text data are broken down into small units called tokens, which can be individual words or language characters. For instance, the text “Thank You!” can be tokenized into: “thank”, “you”, “!” and “ “.
Afterward, the model “self-trains” to predict the next token(s) (using associated probabilities of tokens) in a sequence of textual inputs.
Here’s a simple example. Suppose the model will predict the next word in the sentence “The apple falls from the…”
The probability distribution of tokens under this context (and based on the LLM predefined vocabulary from its training data) are as follows:
“table”: 0.22
“tree”: 0.42
“Basket”: 0.12
(other tokens with low probabilities)
The model predicts “tree” as the next word because it has the highest associated probability of occurrence.
Once trained, an LLM becomes capable of producing high-quality phrases and sentences by mathematically predicting the succeeding combination of tokens with the highest probability of “satisfying” the textual prompt. That’s why LLMs arecomputational,as they generate responses based on their calculation of tokens’ probabilities.
So, when you ask ChatGPT who gave the Gettysburg Address, the model will respond that it's “Abraham Lincoln.” This is not because it’s cognizant of the terms “Gettysburg,” “Address,” “Abraham,” or “Lincoln,” but because it generates the most probable response based on the patterns extracted from its training data.
What Is an LLM Chatbot?
LLM chatbots are conversational programs or interfaces powered by large language models. These chatbots mimic human language as they provide real-time, accurate, and coherent responses.
Unlike old-school, rule-based chatbots, LLM chatbots are more flexible and interactive. Rather than relying on predefined scripts or rules, they draw upon their training data to provide personalized and contextual responses.
More remarkably, LLM chatbots perpetually self-improve as they “talk” with users. The more conversation they have, the better their responses become. Of course, you can also “manually” fine-tune LLM for the chatbot and improve its performance by retraining it with fresh and improved data.
With their agility, versatility, and conversational capacity, LLM chatbots are revolutionizing various industries like healthcare, logistics, finance, and education.
LLM chatbots propel the quality of a business’s support service in different ways.
24/7 Virtual Assistance
LLM chatbots can address customer concerns even beyond business hours, helping you gain their trust and loyalty in the long term.
2. Cost-Saving Service Solution
Instead of hiring additional staff to answer a surge of customer requests, get a chatbot that can simultaneously address them. This approach is much cheaper and more efficient. Using conversational chatbots is estimated to decrease support costsby 30%.
3. Personalized Support Service
LLMs are trained to “comprehend” language nuances, enabling them not only to understand words but also to infer the intent behind them. LLM chatbots pick up customers’ tones, sentiments, and specific needs to provide tailored responses.
4. Multilingual Capabilities
Chatbots are commonly trained in the English language, but you can use textual training data in any language. Once trained, you can deploy them to address your global customers.
LLM Chatbot Examples in Customer Support
Here are some LLM chatbots made for customer support.
My AskAI
My AskAI’s support agent is an LLM chatbot that can deflect up to 75% of daily customer inquiries. By adding your web pages and knowledge bases, My AskAI can automatically answer company-specific inquiries. You can also install it directly onto your existing live chat platform, such as Zendesk, HubSpot, or Intercom.
Intercom Fin
Intercom is one of thebest customer support platformsusing an LLM chatbot named Fin. You can train it using your URLs, help centers, and articles. Upon deployment to your site, it can address basic inquiries or escalate complex concerns to agents.
Aivo Agentbot
Aivo’s Agentbot is a no-code AI chatbot that you can install on your Meta business platforms (Facebook and Instagram) to address simple customer concerns automatically.
Amazon Lex
Amazon Lex harnesses LLM technology to provide customers with accurate product information and assist them with their queries.
Tidio Lyro
Lyro chatbot can be developed using ready-made, no-code templates. You can use it to provide 24/7 customer support, automate lead generation, and provide personalized customer recommendations.
Conclusion
In the ever-competitive business landscape, LLM chatbots are game-changers for stepping up your customer support and winning your clients’ loyalty, trust, and satisfaction.
My AskAIis an affordable LLM chatbot that can provide instant answers to any support questions. It can be developed with no coding required—add your internal company content and knowledge bases (from Notion or Google Drive) and integrate them into your support and workplace apps.
Mike is an experienced Product Manager who focuses on all the “non-development” areas of My AskAI, from finance and customer success to product design, copywriting, testing and more.
Alex is an experienced CTO and founder who largely focuses on all the technical areas of My AskAI, from AI Engineering, Technical Product Management and overall Platform Development.