How do chatbots work - chatbots ultimate guide 2024

Remember the good old days when Microsoft’s “Clippy” graced our desktop screens, answering questions and making suggestions playfully?

You may call it the earliest version of a chatbot. It was helpful but basic and limited in its ability to answer questions. Modern chatbots have come a long way since then. They are faster, more accurate, and have a much more vast knowledge base.

Due to their increased efficiency, chatbots have become an essential tool for businesses. According to statistics:

• Chatbots have now taken over almost 65% of B2C interactions for businesses.
• Retail chatbots saved over $11 billion in transaction costs in 2023.
• Every one out of six customer service help you may have gotten this year, was through a chatbot.

So how do chatbots work? What is the science behind them? In this blog post, we are discussing the behind-the-scenes of chatbots. We’ll discover how AI chatbots work, learn what NLP is, and conclude with the best AI chatbot solution in the market for your business.

So are you ready? Let’s start reading!

What is a chatbot?

A chatbot is essentially a robot you can chat with (as the name implies). Imagine you have a question or need help with something, and instead of waiting on hold or searching through endless FAQs, you just type your query into a chat window. Instantly, the chatbot responds—almost like having a conversation with a knowledgeable customer service representative.

A chatbot is programmed to simulate a conversation between humans. It’s designed to understand your questions and provide helpful answers, often mimicking human conversation. Whether you’re ordering a pizza, booking a flight, or getting advice on what to wear for a rainy day, a chatbot can handle it. 

Some are simple, answering straightforward questions, while others are more advanced, capable of complex interactions and even learning from past conversations to improve over time.

Because of how chatbots work, their superpower is their availability. They’re always on, ready to assist whether it’s day or night. And because they’re designed to be user-friendly, you don’t need to be tech-savvy to interact with them.

📖 RELATED: Best AI chatbots for your business + how to pick the right one for you

What are the main types of chatbots?

Did you know? A well-designed chatbot can handle 80% of simple customer service queries without any problem.

But before we get into how a chatbot works, let’s learn what are their types.

1. Rule-based chatbots:

These are the straightforward ones—they follow a set of pre-defined rules to guide the conversation. Think of them like a flowchart in action. If you ask about store hours, they’ll give you the hours. If you ask something they’re not programmed for, they might politely guide you to a human. 

Pros: They’re great for handling specific, routine tasks and are fairly easy to develop.

Cons: They can answer only a limited number of questions.

Example: Imagine you’re on a retail website, and you open the chat window to ask about their return policy. The chatbot might respond with a straightforward, pre-programmed message:

“Our return policy allows returns within 30 days of purchase with a receipt. Would you like to start a return?”

2. AI-powered chatbots

These are the more advanced cousins of rule-based bots. AI-powered chatbots use artificial intelligence and natural language processing to understand and respond to more complex queries. They can handle a wider range of topics, learn from past interactions, and even offer personalized recommendations. It’s like chatting with someone who really gets you.

Pros: They can handle a wide range of queries.

Cons: They are much more advanced and require technical development.

Example: Let’s say you’re using a travel app to plan a vacation. You ask the chatbot for hotel recommendations in Paris. Instead of just giving you a list, the AI-powered bot considers your past preferences, budget, and even weather conditions to suggest the best options:

“Based on your previous stays and the current weather in Paris, I recommend the Hotel de Paris. It’s within your budget and has excellent reviews for its cosy winter amenities.”

3. Hybrid chatbots

Next, we have hybrid chatbots. These combine the best of both worlds—starting with rule-based interactions but seamlessly switching to AI when things get more complicated. They’re perfect for businesses that need the reliability of rule-based bots but also want to offer a more sophisticated experience.

Pros: They combine the strengths of both rule-based and AI-powered chatbots.

Cons: Maintaining rule-based scripts and training the AI component can be resource-intensive

Example: You’re booking a flight online, and you start by asking the chatbot about available flights. The conversation begins with a rule-based interaction:

“Please enter your departure city.”

As you proceed and ask more complex questions like, “What’s the best seat for someone who gets motion sickness?” the chatbot seamlessly transitions to its AI component:

“I recommend a seat over the wing. It tends to experience less turbulence. Would you like me to select that for you?”

What is a chatbot architecture?

To understand how a chatbot works, you need to understand the basic principle behind its operation.

Chatbot architecture refers to the underlying framework and components that allow a chatbot to function. It is like a blueprint or system design that determines how the chatbot understands, processes, and responds to user inputs. A well-designed architecture ensures the chatbot works smoothly, handling conversations in a way that feels natural and efficient.

At the core, chatbot architecture typically includes several key components:

1. Question and answer system

How does a chatbot answer your questions?

Chatbots have a Q&A system that helps them understand your questions and generate an appropriate response. Their Q&A system can be built by training them in two ways:

• Manual training: The chatbot is fed the most frequently asked questions and their accurate answers. Manual training enables the chatbots to answer these questions quickly and with preset answers that you can change at your will.
• Automated training: You upload a set of company documents into the chatbot’s system and ask it to train itself. The bot will use the company database to come up with possible questions and their answers automatically.

2. Natural Language Processing (NLP) Engine

This component is the brain of the chatbot. It’s responsible for understanding and interpreting the user’s language. The NLP engine breaks down the user’s message into understandable parts, identifies intent, and extracts relevant information, like names, dates, or preferences.

The engine has two parts:

• Intent classifier gauges what the user is asking and how their query can be appropriately answered.
• Entity extractor maps the keywords in the user’s query and uses them to generate a response.

The chatbots can also incorporate feedback from the users and use it to improve their performance. They also use policy learning to detect which conversations end in happy user results.

3. User Interface (UI)

This is the front-end part where users interact with the chatbot. It could be a chat window on a website, a messaging app, or even a voice interface like a smart speaker. The UI captures user inputs, whether typed or spoken.

4. Data storage

This component stores conversation histories, user preferences, and other relevant data. It allows the chatbot to maintain context across sessions, personalise interactions, and improve through analytics.

5. Custom integrations

Need to tailor your chatbot according to your peculiar needs? Custom integrations help integrate your chatbot with your CRM, company database, calendar, payment routes, and several other tools.

How do chatbots work?

Chatbots work by having a conversation with you, just like you would with a real person, but through a computer or smartphone.

Here’s a simple explanation of how things work:

• Understanding your message: When you type or say something to a chatbot, its first job is to understand what you’re asking. For example, if you type, “What’s the weather today?” the chatbot needs to recognise that you’re asking about the weather.

• Figuring out what you want: After understanding your message, the chatbot tries to figure out what you want. This might sound simple, but it’s like solving a little puzzle. The chatbot looks at the words you’ve used and determines your “intent.” In this case, your intent is to get a weather update.

• Finding the right information: Once the chatbot knows what you’re asking for, it goes to work to find the right answer. It might check a weather database, look at previous conversations, or connect to other online services to get the information you need.

• Responding to you: After gathering the necessary information, the chatbot puts together a response. If you asked about the weather, it might say, “Today’s forecast is sunny with a high of 75°F.” The goal is to give you a clear and helpful answer.

• Keeping the conversation going: The chatbot also keeps track of the conversation so it can respond appropriately if you ask a follow-up question. For example, if you then ask, “What about tomorrow?” the chatbot remembers that you’re still talking about the weather and gives you the forecast for the next day.

• Learning over time: Many chatbots get better over time. They learn from each interaction, noticing patterns and improving their responses. For instance, if a lot of people ask about specific topics, the chatbot might start giving more detailed answers on those subjects.

The above steps may sound simple, but there are a lot of technical details that run the process. Let’s break down how chatbots work by explaining three key concepts: pattern matchers, algorithms, and artificial neural networks.

1. Pattern matchers: spotting familiar phrases

Have you ever solved a jigsaw puzzle puzzle? The thing works when you spot pieces that fix together and continue doing that. Similar to what pattern matchers do in chatbots.

• How they work: Pattern matchers look for specific phrases or words in what you type. For example, if you ask a chatbot, “What time is it?” the pattern matcher recognises the word “time” and matches it with a pre-set response like, “It’s 2 PM.”

• Example: Let’s say you type “Tell me a joke.” The chatbot has a pattern matcher that recognises the word “joke.” It matches this pattern with a stored response like, “Why don’t scientists trust atoms? Because they make up everything!”

Pattern matchers are great for handling simple, straightforward questions, but they’re limited because they only respond to exact matches. If you phrased the same question differently, the chatbot might not recognise it.

2. Algorithms: step-by-step problem solvers

So how does a chatbot work out more complex questions? The secret lies in algorithms. Just like a step-by-step recipe you follow while baking a cake, an algorithm is a set of detailed instructions that tell the chatbot what to do with the information it receives.

• How they work: When you ask a question, the chatbot uses algorithms to process your words, figure out your intent, and then decide on the best response. These algorithms break down your input into smaller parts, analyse them, and follow a logical process to find the answer.

• Example: If you ask, “What’s the weather like tomorrow?” an algorithm in the chatbot first picks out keywords like “weather” and “tomorrow.” Then, it uses a series of steps (or rules) to search for tomorrow’s weather forecast and present it to you in a clear way.

Algorithms are more flexible than pattern matchers because they don’t need an exact word-for-word match. They’re great at handling tasks that require a bit more thinking, like finding specific information or performing simple calculations.

3. Artificial neural networks: like the human brain but in a robot

Pattern matchers and algorithms are cool but how do AI chatbots work? Artificial neural networks are what make an advanced chatbot “advanced.” Inspired by one of the greatest creations of nature, these networks work like a human’s brain and nervous system.

Just like our brains learn from experiences, neural networks help chatbots learn from past conversations to get better at understanding and responding.

• How they work: A neural network is made up of layers of “nodes” (think of them as tiny decision-makers). When you send a message, the chatbot passes it through these layers. Each node processes the information a bit, figuring out the most likely meaning of your words. The network learns over time by adjusting its understanding based on whether past responses were right or wrong.

• Example: Suppose you often ask a chatbot, “What’s up?” to check the news. Initially, it might not get it right. But as it learns from each interaction, the neural network starts recognizing that “What’s up?” means you’re looking for news updates. Soon, it’ll be able to provide a relevant answer like, “Here’s what’s happening today: [news highlights].”

Artificial neural networks can handle more complex conversations, adapt to different ways of asking the same question, and even understand context and subtle nuances in language.

What is NLU (Natural Language Understanding)?

NLU is how a chatbot understands what you are asking. It is a key part of how chatbots and other AI systems comprehend and respond to human language.

NLU allows the chatbot to not just hear or read your words but to actually grasp what you mean by them.

To break it down, NLU works through these 3 concepts:

• Identifying entities: NLU picks out important details in your message, known as "entities." If you say, “Book a flight to New York on Friday,” the entities are “New York” (destination) and “Friday” (date). These details are crucial for the chatbot to carry out your request accurately.

• Understanding intent: When you talk to a chatbot, you usually have a specific goal or "intent." For example, if you say, "I need a pizza," your intent is to order food. NLU helps the chatbot figure out what that goal is. It recognises that your request isn’t just random words but a specific action you want the bot to take—like placing an order.

• Dealing with context: Sometimes, a single message isn’t enough to understand what you want. NLU helps the chatbot keep track of the context of a conversation. If you ask, “How about Saturday?” after talking about booking a flight, the chatbot uses NLU to know that you’re still discussing travel plans.

Why NLU is important?

NLU is what makes chatbots feel more human-like. Without it, the bot would only respond to specific, pre-programmed phrases. NLU allows the chatbot to be flexible, understanding a wide range of questions, even when they’re asked in different ways. It’s what enables the bot to respond accurately and make the interaction feel natural.

What Is NLP (Natural Language Processing)?

Natural Language Processing (NLP) is the broad field of study and technology that focuses on how machines, like computers and chatbots, can understand, interpret, and respond to human language in a way that’s both meaningful and useful. Think of NLP as the bridge that allows humans to communicate with machines using everyday language, whether spoken or written.

Why NLP is important?

NLP is essential because it allows machines to interact with us in our language, not theirs. Without NLP, we’d have to communicate with computers in a way they can easily understand—like using code or commands. NLP makes the interaction feel more human, allowing us to talk to machines as we would with another person.

For example, when you ask your smartphone, “What’s the weather like today?” NLP processes your question, understands you want a weather update, checks the relevant data, and then generates a response like, “It’s sunny with a high of 75°F.”

The Bigger Picture: NLP vs. NLU

While NLU (Natural Language Understanding) is a part of NLP focused specifically on understanding and interpreting meaning, NLP covers a broader scope. It includes not only understanding but also generating language, translating languages, summarising texts, and more. Essentially, NLP encompasses all the techniques and processes that allow machines to work with human language in an intuitive and effective way.

Chatbots vs. AI chatbots

The advent of powerful chatbots like Chat GPT and Gemini has mixed the terms of chatbot and AI chatbots. But a few years back, when technology wasn’t as advanced as today, these terms could not be used interchangeably.

A standard chatbot follows pre-programmed rules and scripts to respond to user inputs. It matches specific keywords or phrases with set responses. These chatbots are great for simple, repetitive tasks, like answering common questions or guiding users through basic processes, but they can struggle with more complex or unexpected queries.

On the other hand, an AI chatbot uses artificial intelligence, particularly natural language processing (NLP) and machine learning, to understand and respond to user inputs more flexibly. AI chatbots can handle more complex conversations, learn from past interactions, and provide personalised responses. 

Here is a side-by-side comparison of how AI chatbots work and how standard ones do:

Integrating chatbots with digital messaging channels  

Chatbots play a crucial role in the digital marketing of a brand by engaging customers, improving customer experience, and driving conversions. Messaging channels like WhatsApp, Facebook Messenger, Instagram DMs, and website live chat are a few ways these bots can work out their magic.

• Through messaging channels, chatbots can interact with potential customers in real time, answering questions, guiding them through the buying process, and even making personalised recommendations based on user behaviour.
• By providing instant responses and 24/7 support, chatbots help keep customers engaged, reduce bounce rates, and increase the chances of converting visitors into leads or sales.
• They can also leverage these channels to collect valuable data on customer preferences and behaviour, which can be used to tailor marketing strategies and improve targeting.

Trengo: The ultimate AI chatbot solution for your website and more

So now that you know how an AI chatbot works, are you ready to get one for your website? Because Trengo has the perfect solution for you!

With Trengo’s AI-powered chatbot “AI HelpMate,” you can bring all your messaging channels under one roof. Whether your customers reach out through WhatsApp, website chat, or social media, all your customer service queries are in one inbox.

AI HelpMate helps you:

• Handle routine conversations while driving support and sales.
• Offer support in 26 languages with 6 default languages in case of uncertainty.
• Generate suggested responses for team members to use and edit.
• Seamlessly switch over the conversation to human agents when needed.
• Adapt to the branding guidelines and knowledge you use to train it.

So are you ready to learn how do chatbots work when they are smart and reliable like Trengo? Book a demo today to check out for yourself.