Chatbots provide instant, human-like responses to customer queries. Their efficiency isn’t magic; it’s the result of a structured technical architecture. Understanding how a chatbot works, from interpreting intent to retrieving data via Retrieval‑Augmented Generation (RAG), is essential for building a tool that actually drives ROI. This article breaks down the core components of chatbot technology, i.e., chatbot architecture, and how each type operates in a business environment.

What is a chatbot?

A Chatbot is a computer application or program that uses Natural Language Processing (NLP) and Machine Learning (ML) to interact with users and simulate a human conversation to answer queries, collect information, provide resolutions, guide visitors through offerings, and more.

Now, how does a chatbot work?

How chatbots work

A chatbot acts as a bridge between a user’s natural language and a computer’s structured data through these simple steps:

  1. Input: The user types a message or speaks a command.
  2. Analysis: The bot breaks the sentence down to understand the command’s intent.
  3. Processing: The bot searches its database or logic tree for the best answer.
  4. Output:The bot converts the resulting answer back into a human-readable response.

Chatbot architecture

To understand how a chatbot works, we have to look at how it processes. A modern chatbot architecture comprises several specialized layers working in sync.

Channel/UI layer

This is the front-facing interface where the interaction happens, such as a website chat widget, WhatsApp, or a mobile app. It captures the visitor’s raw input and displays the final response.

Natural Language Understanding

Natural Language Understanding (NLU) is the engine that decodes human commands.

  • Intent: What the user wants (e.g., converts "Where is my order?" into order_tracking).
  • Entities: Specific details (e.g., converts "Order #14263" into order_id).
  • Sentiment: The emotional tone (e.g., converts "I am frustrated" into negative and needs immediate attention), allowing the bot to adjust its empathy level.)

Dialog manager with context detection

The dialog manager acts as the moderator, keeping track of the conversation and ensuring that the flow is logical and stays within the context. For example, if someone asks, ‘‘How much is it?’’ after asking about a specific product, the dialog manager knows ‘‘it’’ refers to that product.

Knowledge base and associated tools

A bot is only as smart as the data fed into it. This layer connects the bot to:

  • Knowledge bases (KB): For answering FAQs.
  • CRM/helpdesk: To look up the customer’s history, subscription info, past interactions, ticket status, and more.
  • APIs/webhooks: To perform custom actions that require any third-party access or require input from a different application.

Response generation

Response generation determines how the bot speaks back, depending on its sophistication:

  • Templates: Pre-written, static replies.
  • Natural Language Generation (NLG): Dynamically generated text based on the input and context.
  • Large Language Model (LLM) + RAG: LLMs combined with RAGs will help provide highly accurate, conversational answers based on specific requirements and keep them contextual.

Monitoring and retraining

Because every conversation the chatbot has is a learning opportunity, Machine Learning (ML) loops monitor failures or conversations where the bot has replied ‘‘I don’t know’’ and pass them to developers to retrain the bot for better accuracy next time.

Types of chatbots

Since the inception of chatbot technology, many types of chatbots have become mainstream, and the major and still relevant among them can be combined together under the following types:

  • Flow chatbots: Rule-based bots that follow a strict ‘‘if-this-then-that’’ logic.
  • FAQ chatbots: Specialized bots that search documents to provide instant answers.
  • AI chatbots:

    • Advanced bots using LLMs to hold fluid, human-like conversations.

  • Hybrid chatbots:These combine the best of both worlds. They use a structured flow for tasks (like booking an appointment) but switch to ‘‘AI/FAQ’’ mode when a user asks a random question.

Comparison table: Types of chatbots

  • Types of chatbotsFlow chatbotsFAQ chatbotsAI chatbotsHybrid chatbots
  • Best forSimple data collectionAnswering common questionsComplex, empathetic chatsData collection and versatile business use cases
  • Data requiredLogic tree/scriptsKnowledge Base (KB)Large datasets/LLMsKB and decision trees
  • Failure modesRigid. It breaks if user deviatesMisses nuances/synonymsHallucinates (if the instructions aren’t set clearly)Logic gaps in transitions
  • Time-to-launchVery fast (in a day)Fast (couple of days if KB is readily available)Medium (a week)Medium (a week)
  • Human hand-offWhen user asks for hand-offAfter 1 or 2 failed matchesNegative sentiment/complex queriesAfter logic flow completes
  • SalesIQ’s bot optionsZobot, our no-code bot building platformAnswer BotAnswer Bot with Zia or ChatGPT integrationHybrid bot

How each chatbot type works

Flow chatbots

Flow chatbots/rule-based chatbots don’t understand the conversation’s context because they operate according to predetermined rules. Because the flow is already established, the architecture of flow chatbots is straightforward. To identify the components involved in the flow of chatbots, they can be broken down into the same three stages we discussed earlier.

Stage 1: Understanding the user's input

When a user initiates a conversation with the flow chatbot:

1) The tokenizer splits the input into readable words or phrases for the machine to understand.

2) The broken words or phrases are matched with the patterns stored in the flow chatbots using a pattern matcher.

The tokenizer and the Pattern Matcher have limited potential in rule-based flow chatbots because users will have limited scope to enter entirely new queries independently. Most of the time, options will be shared with the users, and they'll be asked to select from them. They'll be taken to a different flow based on what they choose.

Stage 2: Searching the database

Before deploying flow chatbots, a decision tree or flow-based logic will be written to define how the flow chatbot should work.

For example, If the user inputs the option "I'm yet to receive my order," the pre-defined flow will show the next option, "Can you please enter your order number," so that it can search through the orders to get the information needed by the user.

The dialogue manager doesn't have a prominent role here, as the chatbot follows pre-defined rules. The rules themselves establish the context of the conversation, and it's not dynamic.

Stage 3: Drafting and sharing replies

The flow of chatbots' replies will mostly be static, which is also determined when the rules are established. However, variables can be included to personalize it to a certain extent.

For example, when the flow chatbot asks for the user's name, the input can be stored under the variable %name%, which can be later used to personalize the conversation. When the order status is retrieved from the database, it'll send the templated response "Your order is on the way, John. If you'd like to track it, click the link below."

On the backend, it can integrate with other applications or use API calls to get the information the user asks for. After retrieving the data, it'll be shared in a templated response.

FAQ chatbots

Traditional FAQ chatbots have the additional scope of allowing users to type their query instead of just selecting the shared options. The breakdown of its architecture will look like:

Stage 1: Understanding the user's input

The input will first be cleaned and normalized from the raw text. The tokenizer and the Pattern Matcher will break down the complex format into machine-readable ones. The input will also be passed through a synonym and spell-check engine that will parse and figure out the variations and spelling errors.

It'll give the chatbot a better understanding of what the user is asking. Since the chatbot will be integrated with a large volume of business collateral, such as articles, knowledge base resources, FAQs, and more, it needs to identify what the user is asking to figure out what to share with them.

Not everyone will have the same way of phrasing their query. So, by breaking down the phrases into simpler ones and passing them through the synonym and spell-checker engine, the chatbot will be able to identify the variations to search through the database to retrieve the information the user is looking for.

Stage 2: Searching the database

Once the query is broken down, the identified keywords will be searched across the database using Keyword Matching. However, matching keywords has a downside: it marks all the documents that have the particular keyword. Even matching all the identified keywords won't guarantee a relevant answer for visitors.

To nullify this, FAQ chatbots will also do Pattern Matching, which matches the entire phrase with what's available in the database. When all phrases match, accuracy will be better than just keyword matching.

If no other entries in the database match the keywords/phrases, the system will retrieve the information and move to the next stage. But if multiple entries match the user's query, Matching Logic will be introduced to identify which resource to share.

The logic can be based on Specificity ranking (it gives precedence to more specific patterns over the generic patterns identified with the keywords), matching keyword counts (the entry with the most number of matching keywords will be given precedence), or confidence score (it'll give one point per matched keyword in the selected entries and share the one that gets the maximum points). Based on the logic used, the entry will be selected for sharing.

But what does the chatbot do when it can't figure out which one to share?

To handle such cases, the chatbot will be programmed with ambiguity matches. It'll share the most related entries identified with the users, leaving it up to them to get the answers. The bot will also have a fallback mechanism when it's not able to identify the match. It'll inform the user that it's not able to identify what they're looking for and ask them to be more specific.

Even though FAQ chatbots aren't powered by AI, they log all the conversations happening with them. In cases where they can't figure out what to share, they log those and share them with the technical team for further improvements.

Stage 3: Drafting and sharing replies

Once the answer is retrieved, the content users are looking for will be shared with them. The default formatting and rich content that needs to be shared will be pre-programmed into it, and the answer templates will be shared.

AI chatbots

One of the dynamic shifts that happened with the introduction of AI chatbots is the accuracy of the answers shared with the users and the conversational tone in interacting with them.

Stage 1: Understanding the user's input

In AI chatbots, in addition to the components we discussed above with the FAQ chatbots, the NLU comes with two more components: Intent Detection and Entity Recognition.

With Intent Detection, the chatbot tries to understand what the user is trying to achieve. To do this, modern intent detection classifiers use algorithms like logistic regression or neural networks that are trained with data.

What does Entity Recognition do here? It extracts the specific keywords from the user's inputs and uses them to refine the conversation's intent.

Stage 2: Searching the database

In searching the database, the technology that uplifts the performance and accuracy of AI chatbots compared with FAQ chatbots is Retrieval-Augmentation Generation (RAG), i.e., how the chatbot searches and retrieves information from the database for better, more accurate responses. With RAG, during the retrieval stage, an AI chatbot will first look for relevant information from the database by matching keywords or phrases. It'll retrieve the top relevant answers by doing the first level of search.

In addition, it'll also search the external knowledge source for relevant information, which makes it an open-domain QA system. When searching for information only from the added collateral, there's a chance the collateral isn't updated and contains outdated information. So, by doing an external search, RAG will have information from both attached and external sources. It'll combine them and pass them on to the next stage for processing and sharing with the users.

Because of the additional search capability, AI chatbots' answers are more accurate than traditional FAQ chatbots'.

Stage 3: Drafting and sharing replies

AI chatbots will have two options for drafting and sharing replies. Either the NLG engine will compile the retrieved information and share it with the users or pass it through the GPT engine for better and fluent responses.

How the drafting and sharing stages will be handled can be decided based on the business use case and the user's expectations. RAG also has its own memory. It stores the context of the present conversation and allows the dialogue manager to maintain it for the following exchanges.

ML will be deeply integrated into AI chatbots, as it allows users to learn from conversation history and gain contextual intelligence.

Related read: 

How to measure ROI for chatbots in 5 steps

Hybrid chatbots

Hybrid chatbots are the most versatile type, combining the structured efficiency of rule-based flows with the dynamic conversational power of AI. This allows a business to automate specific workflows while still answering open-ended questions.

Stage 1: Understanding the user’s input

The interaction typically begins within a pre-defined flow (rule-based). The chatbot presents specific options or asks for specific data, like an email address or company size.

However, the hybrid model uses switching logic. If the user types a query that doesn’t match the expected flow inputs, the dialog manager immediately triggers the NLU engine. The NLU analyzes the intent and entities of the unexpected message to determine if the bot should break away from the script to provide a direct answer.

Stage 2: Searching the database

Once the NLU identifies an out-of-flow query, the chatbot searches the integrated knowledge base. In a hybrid setup, this often involves a two-tiered search:

Keyword/pattern matching: To find direct answers in the FAQs.

AI-powered retrieval (RAG): If the answer isn’t a simple keyword match, the bot uses RAGs to scan complex documents and extract a precise response.

Stage 3: Drafting and sharing replies

After retrieving the relevant information, the bot shares the answer using either a pre-set template or a generated response.

The defining characteristic of a hybrid chatbot is contextual resumption, which occurs immediately after this. Because the dialog manager maintains the state of the initial conversation, the bot seamlessly returns the user to the exact point where they left the original flow. For example, after answering a random question about ‘‘Pricing,’’ the bot will say, ‘‘Now, back to where we were: What’s your monthly website traffic?’’.

Chatbot examples in practice

Support deflection

When a customer asks, ‘‘How do I reset my password?’’ Instead of a human agent answering, the FAQ chatbot identifies the intent, pulls the ‘‘Reset password’’ article from the KB, and shares the steps. It results in instant resolution and zero wait time for the customer and zero effort for the human agents.

Lead qualification

A visitor lands on your pricing page. A flow chatbot triggers: ‘‘Hi! Looking for a quote?’’ It asks for their company size and budget. If the lead is a high-value, high-intent lead, based on their input, the bot automatically routes the chat to a salesperson to take over the conversation and the deal.

Build chatbots with Zoho SalesIQ

Zoho SalesIQ is a chatbot builder that lets you build any chatbot for your business.

How to build the chatbot with SalesIQ

  • Zobot, our no-code bot builder:Use the drag-and-drop interface to create flow chatbots. Simply connect the ‘‘cards’’ (input cards, action cards, response cards, AI cards, visitor info cards, integration cards, end cards) to map out your customer journey.
  • Answer bot: Upload your existing help articles or FAQs. SalesIQ’s NLP will automatically learn your content to answer visitor queries.
  • Hybrid bot: Use the ‘‘Answer Bot’’ card within the no-code builder. This allows your bot to handle structured data collection while remaining ready to answer FAQs at any moment.
  • OpenAI integration: Connect your ChatGPT API key to SalesIQ to transform your bot into a generative AI powerhouse that uses RAG to stay accurate to your brand.

If you’d like to see SalesIQ in action, you can sign up for a 15-day trial.

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FAQs on how a chatbot works

What happens when a chatbot doesn't understand a question?

When a chatbot doesn't understand a question, irrespective of the type of technology that enables it, a fallback mechanism will be looped into the conversation's flow. The bot will try to understand the user's query better, and if it's still not able to provide a resolution, the query will be transferred to the available human agent.

How does a chatbot integrate with my CRM, help desk, and other business applications?

Chatbots integrate with your CRM, help desk solutions, and other business applications via their built-in connectors. If direct integration isn't available, API calls and webhooks can integrate chatbots with your existing tech stack.

What's the difference between a chatbot and a virtual assistant?

While chatbots, typically with a chat-based interface, use rule-based or decision trees to collect information or answer user queries, virtual assistants use technology in the form of voice or text to handle personalized requests and broader functions like completing tasks and even controlling smart-home devices.

Read more about AI chatbots vs virtual assistants.

Are chatbots AI?

Not all chatbots are AI chatbots. Flow-based or rule-based chatbots are simple software programs following a script. However, modern FAQ and conversational bots use artificial intelligence (NLU and ML) to understand and reply to queries.

What is RAG, and why does it reduce wrong answers?

Retrieval-Augmented Generation (RAG) forces an AI to look at your trusted documents before it speaks. Instead of guessing (or worse, hallucinating) based on its general training, it finds the specific answer in your database first, significantly reducing wrong answers.

When should a chatbot hand off to a human agent?

A hand-off should occur when the bot can't resolve the query or when sentiment analysis detects high user frustration. This ensures a high customer satisfaction (CSAT) score by providing human empathy exactly when needed.

How do chatbots integrate with websites/CRMs?

Because SalesIQ has direct integrations with Zoho CRM, Bigin by Zoho CRM, and Salesforce CRM, all data transfers and syncs between the bot and the CRM are handled natively. In other cases, chatbots connect with CRMs via APIs and webhooks. When a bot collects a lead's email, it sends the data via an API to your CRM, creating a new lead record automatically.

Can you train and customize the chatbot to match your business needs?

By adding your business-related articles and resources, you can train a chatbot to answer visitors' queries in a personalized way. It can also be trained to define your responses, set workflows, and tailor the tone and branding of your business.

What's NLU vs. NLP vs. NLG?

  • Natural Language Processing (NLP): The broad field of AI focused on human-computer interaction.
  • Natural Language Understanding (NLU): This part figures out what the user meant and understands the context.
  • Natural Language Generation (NLG): NLG determines how to express the answer in a way humans would understand.

What data do you need to train a chatbot?

You primarily need two things:

  1. Intents and entities: Examples of how users ask questions.
  2. Knowledge base (KB): Your organization's documentation, PDFs, or website links that contain the actual answers.