Chatbots vs. LLMs

Introduction

As artificial intelligence advances, chatbots have become a primary interface for interacting with large language models (LLMs). Whether it's ChatGPT, Mistral, Cohere, Anthropic, or Perplexity, these systems all rely on chatbots to connect users with the underlying LLMs.

But why use chatbots instead of directly engaging with LLMs? This document explores the key reasons, technical distinctions, and performance differences between chatbots and standalone LLMs.

1. Introduction
2. What are Chatbots?
3. What Are Large Language Models (LLMs)?
4. Why Use Chatbots? Memory is Key
5. Examples of Outputs: How Chatbots Differ From LLMs
6. Concepts Related to Both Chatbots and Large Language Models
   • Context Length
   • Tokens
7. Choosing the Right Model: Chatbots vs. Non-Chatbots
8. Conclusion
9. FAQ

What are Chatbots?

Chatbots are AI systems that simulate human-like conversations, enabling natural, multi-turn interactions. Unlike standalone LLMs, which process single inputs, chatbots are designed to handle ongoing dialogues, remembering previous exchanges to create coherent, contextually relevant responses. This makes them ideal for tasks requiring conversational context, such as customer service or multi-step problem-solving.

What Are Large Language Models (LLMs)?

LLMs, like GPT-4o, Llama-3, Mistral-7B, and Claude 3.5, are the powerful engines behind chatbots. They process language and generate responses, but they don't inherently manage conversation continuity or remember previous interactions.

For example, standalone LLMs respond solely to the most recent input without considering prior context. While highly effective for specific tasks like text generation or answering questions, these models are less suited for interactive, dynamic conversations where memory and context retention are crucial.

Why Use Chatbots? Memory is Key

A key feature of chatbots like ChatGPT is their ability to maintain memory, or simulated context, across interactions. This memory allows the chatbot to recall previous messages in a conversation, making it possible to answer follow-up questions, provide clarification, and support multi-turn interactions more effectively.

Why Memory Matters?

Memory (or simulated context) in chatbots:

• Allows for follow-up questions: ChatGPT remembers your previous questions, making follow-up questions and clarification easier.
• Mimics human interaction: Conversations feel more natural because the model responds with contextually relevant answers.
• Supports multi-turn interactions: Chatbots are ideal for customer support or complex problem-solving, where multiple exchanges are needed.

Example of Outputs: How Chatbots Differ From LLMs

Here is an example that shows you how GPT-4o differs from ChatGPT:

We can see that GPT-4o has completed our input with what it felt were the most probable next characters. On the other hand, ChatGPT has responded to us as if we suddenly stopped speaking in a conversation. The conversationality of chatbots makes using them feel more natural to use, so most people prefer them over other AIs. However, the most significant reason that chatbots are better is that companies like OpenAI and Anthropic have built chatbots that are very smart and can better respond to your prompts.

Concepts related to both chatbots and Large Language Models

Chatbot's memory isn’t like human memory—it’s limited to the current session and is determined by the context length. LLMs also have context length. Context length is measured in tokens.

Context Length

Context length (context size or the context window) refers to the amount of text that a language model can consider when generating a response. For both chatbots and non-chatbots, there is a maximum limit to the context length they can handle. If the conversation or text exceeds this limit, the model will not be able to remember the entire conversation when generating a response. This is why it is sometimes necessary to restate important information or re-prime the chatbot.

Tokens

In both chatbots and non-chatbots, language models don’t read entire words as we do. Instead, they break down text into smaller pieces called tokens. Tokens could be whole words, parts of words, or even characters. For example, the sentence “I don’t like eggs” might be broken down into tokens like I, don, 't like egg s. These tokens are then processed by the model, and each token carries its own numerical representation.

Deciding which model to use is sometimes a trade-off between pricing and the need for longer context lengths.

• Pricing: Many language models, including those from OpenAI, charge based on the number of tokens processed.
• Context length: The maximum number of tokens a model can handle in one interaction is called its context length.

Choosing the Right Model: Chatbots vs. Non-Chatbots

Choosing between a chatbot and a non-chatbot depends on your task.

• Chatbots like ChatGPT are best for:

  • Ongoing conversations: Where follow-up questions and context retention are needed.
  • Complex problem-solving: Where back-and-forth interactions with the model are required.
  • Customer service and support: For resolving issues that require multiple interactions.

• Non-chatbots, such as GPT-4o, are ideal for:

  • Concise tasks: Like completing sentences, generating short answers, or summarizing text in a single instance.
  • When memory isn't needed: Non-chatbots are useful when you need a quick result without the need for conversation history.

Conclusion

Chatbots, like ChatGPT, are the primary means by which we interact with AI today. Their ability to maintain context, mimic human conversation, and support multi-turn exchanges makes them ideal for a wide range of applications. Understanding the differences between chatbots and standalone LLMs can help you choose the right tool for the task at hand, maximizing AI's potential in your interactions.

FAQ

What is an example of a chatbot?

A popular example of a chatbot is ChatGPT, and we will commonly use it in this course.

What makes an LLM a chatbot?

A chatbot has memory, so it remembers previous messages. That's why a conversation with ChatGPT can resemble a human interaction. Other LLMs like GPT-4o can also perform basic tasks like ChatGPT, but they have no memory and therefore are not chatbots.

Why are tokens and context length important?

Tokens represent chunks of text that chatbots process. The context length determines how much previous information a chatbot can remember when generating responses. This directly affects its ability to manage complex, multi-turn conversations.

Valeriia Kuka

Valeriia Kuka, Head of Content at Learn Prompting, is passionate about making AI and ML accessible. Valeriia previously grew a 60K+ follower AI-focused social media account, earning reposts from Stanford NLP, Amazon Research, Hugging Face, and AI researchers. She has also worked with AI/ML newsletters and global communities with 100K+ members and authored clear and concise explainers and historical articles.

Footnotes

1. Sometimes non-chatbots are preferable if you want more concise outputs that complete your text. We will learn about how to access GPT-3 in the next lesson. ↩