How Chatbots work in the era of Large Language Models like GPT4?
This article is part 1 of 2 in the How it Works series.

Estimated reading time: 7 minutes

Welcome to our exploration of how chatbots function in the age of groundbreaking language models like GPT-4. But first, let’s clarify: What exactly is a chatbot? It’s important to distinguish a chatbot from a basic question-answering bot. A question-answering bot is straightforward – you ask a question, and it retrieves and delivers the relevant information. It’s a one-and-done deal; it doesn’t remember past interactions or understand context.

Now, enter the chatbot. It’s like giving memory to a question-answering bot. Chatbots retain details from each interaction, weaving individual queries into a coherent, ongoing conversation. This memory makes them context-aware. They remember what was discussed previously and provide responses based on that ongoing dialogue.

Let’s illustrate this with an example. Imagine you’re on Tesla’s website asking, 

“When will the Cybertruck be available in my area?” 

A simple question-answering bot might tell you, 

“The Cybertruck is expected by April next year,” 

based on your location.

But if you follow up with, 

“Why so late?” 

a basic bot might get confused and ask for more context. 

In contrast, a chatbot understands this as a continuation of your earlier query. It knows you’re still talking about the Cybertruck’s availability and might explain, 

“We’re boosting our production capacity to meet demand. The next expected date is April 2024.”

That’s the beauty of chatbots over basic question-answer systems. They’re not just answering questions; they’re engaging in a conversation, bringing a whole new level of interaction to our digital experiences.

How Chatbots Leverage Large Language Models (LLMs) Today

Having understood the fundamental differences between chatbots and basic question-answering bots, let’s delve into the workings of modern chatbots in the era of Large Language Models (LLMs). A key element that enhances chatbot functionality is ‘memory.’ This memory can be stored in various forms – be it relational databases, NoSQL databases, or even simple files. The crux is to preserve past conversations, enabling the chatbot to retrieve and use this history as context for ongoing interactions.

The strength of a chatbot is often gauged by its memory capacity: how many past interactions it can recall. For instance, in the Lyzr Chatbot SDK, chatbots can remember up to 50 past conversations in the open-source version and up to 200 in the enterprise version, a significant memory span for any chat session. This capability transforms a simple chatbot into an impressively powerful tool.

However, more than memory is needed, especially when creating specialized chatbots for specific business functions like sales, vendor management, procurement, or customer support. This is where the concept of Retrieval Augmented Generation (RAG) comes into play. RAG, empowered by vector databases, is a game-changer in developing focused and efficient chatbots.

The Intricacies of Chatbot Functionality in the Realm of Generative AI

Let’s delve into the fascinating process that underpins the functionality of chatbots powered by Large Language Models (LLMs) like GPT-4. 

image 5
  • The journey begins with data preparation, a crucial step where data is cleaned, curated, and made ready for consumption. 
  • This curated data is then segmented into interconnected chunks, ensuring each piece relates cohesively to the others.
  • Next comes the pivotal role of vector embeddings. These chunks are transformed into vector embeddings using advanced embedding models like OpenAI’s text-ada models or BGE models. 
  • These vectors are then stored in vector databases such as Weaviate or Pinecone, forming the backbone of the chatbot’s knowledge base.
  • When a user poses a question through the chatbot UI, this question is converted into vector embeddings. 
  • This is where the magic of vector search occurs. The chatbot compares the vector print of the user’s query with the vector prints of its stored data. 
  • The most relevant results – the top ‘k’ with the highest similarity scores – are identified. These top results are then fed to an LLM like GPT-4. 
  • The LLM interprets these vectors, understanding the context and the nuances implied by the words. It then crafts a response that is coherent, contextually relevant, and easily comprehensible by humans.

So, what are the key components of this process? They include data curation, chunking, embedding, storing vectors, and retrieving them – collectively known as Retrieval Augmented Generation (RAG). RAG enhances the capabilities of LLMs, leading to more sophisticated and relevant outputs.

Now, integrating the element of memory transforms this system into a full-fledged chatbot. Without memory, it remains a simple question-answering bot. This distinction highlights the chatbot’s role in the broader landscape of generative AI and Large Language Models – a blend of advanced technology and intelligent design, making digital interactions more intuitive and human-like.

Key Factors for Optimizing Chatbot Performance

What are the essential elements that ensure chatbots perform effectively and as expected? At the heart of a high-functioning chatbot lies a couple of critical factors:

  1. Quality of Content for the RAG Engine: The performance of a chatbot is heavily dependent on the quality, diversity, and density of the data fed into the Retrieval Augmented Generation (RAG) engine. Superior data quality enhances the chatbot’s ability to search and retrieve more accurate and relevant responses. The richness and accuracy of the data are paramount for a chatbot to excel.
  1. Underlying Technology: The choice of technology at each step of the chatbot’s data processing pipeline is crucial. This includes the methods used for parsing input data, the strategies for chunking data, the models selected for embedding, and how the indexes are stored. Of particular importance is the selection of the RAG technique. For example, Llama Index has published a variety of RAG techniques, and there are many more available from other research organizations and open-source libraries. The chatbot’s efficacy is a product of the synergistic combination of these RAG techniques, embedding models, and chunk size experiments, along with the inherent capabilities of the vector databases and the LLMs.
image 4

Credits: LlamaIndex

Ultimately, everything boils down to the quality of the data provided to the system from a RAG perspective. High-quality data is the foundation upon which all other technological choices build, determining the overall performance and effectiveness of the chatbot. This interplay of data quality and advanced technology is what makes a chatbot not just functional but genuinely intelligent and responsive.

Experience Chatbot Technology in Action

Ready to see these concepts in real-life application? 

Let’s take a look at a practical demonstration. We’ve utilized the Lyzr ChatBot, an open-source front-end framework, along with open-source SDKs, to train a chatbot using one of Paul Graham’s essays.

Demo link –

You can build your own chatbot in minutes by visiting the link provided below. Here, you have the option to upload PDF files, website links, or YouTube links to quickly create a chatbot tailored to the specific content you provide.

Lyzr Chatbot Builder –

This version is open-source, and while highly functional, it may have some limitations, such as occasional inaccuracies or ‘hallucinations’, as they’re not as powerful as Lyzr Enterprise SDKs. However, the enterprise version of Lyzr, which boasts one of the best chatbot architectures in the market today, takes this technology to the next level. Our enterprise SDKs are designed to automatically determine the optimal chunking size by assessing various chunk sizes and overlaps. They select the most effective embedding based on your data type and pair it with leading vector databases and SOTA LLM models for accurate augmentation.

Additionally, the most applicable RAG technique is carefully selected to match your data and desired output. This adaptive approach ensures that the SDKs can identify the best technological stack for various knowledge retrieval applications, whether it’s for a chatbot, a question-answering bot, document searching, a knowledge base, or even helpdesk automation.

In essence, the Lyzr SDKs are engineered to do the heavy lifting, simplifying the process of determining the most suitable stack for your specific use case and maximizing the effectiveness of your chatbot or related AI application. And you can invoke Lyzr SDKs through a single line of code in your application. That’s the simplicity of Lyzr Technology.

What’s your Reaction?
Series NavigationDesigning a Customer Support Agent with Non-Deterministic LLMs >>
Book A Demo: Click Here
Join our Slack: Click Here
Link to our GitHub: Click Here
Share this:

Similar Posts

Leave a Reply

Your email address will not be published. Required fields are marked *