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1. Chatbot Architecture: How Do AI Chatbots Work?
For many businesses in the digital disruption age, chatbots are no longer just a nice-to-have
addition to the marketing toolkit. Understanding how do AI chatbots work can provide a
timely, more improved experience than dealing with a human professional in many
scenarios.
While many businesses these days already understand the importance of
chatbot deployment, they still need to make sure that their chatbots are trained effectively
to get the most ROI. And the first step is developing a digitally-enhanced customer
experience roadmap. It’s time to take a deep dive into the inner workings of AI chatbots.
How Do AI Chatbots Work So Efficiently?

2. Let’s take a look from the front-end. Most chatbot interactions typically happen after a user
lands on a website and/or when they exhibit the behavior of “being lost” during site
navigation, having trouble finding the information they need.
Once the chatbot window appears – usually in the bottom right corner of the page – the
user enters their request in plain syntax. The chatbot will then conduct a search by
comparing the request to its database of previously asked questions. At the speed of light,
the best and most relevant answer for the user is generated.
Sounds simple, right? There are actually quite a few layers to understand how a chatbot can
perform this seemingly straightforward process so quickly.
What Are the Challenges for AI-Based Chatbots?
To improve their customer service delivery, AI chatbots have to be designed with the
intelligence to evolve and provide more consistent support to customers. There are a few
core challenges with the technology that often need fine-tuning:
• Data Security
As people grow more aware of their data privacy rights, consumers must be able to trust
the computer program that they’re giving their information to. Businesses need to design
their chatbots to only ask for and capture relevant data. The data collected must also be
handled securely when it is being transmitted on the internet for user safety.
• Tone and Voice

3. Getting a machine to simulate human language and speech is one of the cornerstones of
artificial intelligence. Machine learning is helping chatbots to develop the right tone and
voice to speak to customers with. More companies are realising that today’s customers
want chatbots to exhibit more human elements like humour and empathy.
• Sentiment
Similar to the second challenge, sentiment and emotions are also things that AI chatbots
need to understand in order to deal with today’s customers. Businesses are constantly
improving their chatbots’ Natural Language Processing to provide specific kinds of service
and reduce the number of contextual mishaps.
A Beginner’s Guide to Chatbot Architecture
A core component for how AI chatbots work is Natural Language Processing (NLP): the
ability to interpret and analyze human speech, find the right response and reply in a way
that’s understandable in the user’s language.
The goal of NLP is to produce an interaction between computers and humans that’s as
smooth as possible, like a real conversation between two people. NLP achieves this with 3
secondary processes: Natural Language Understanding (NLU), Dialogue Manager (DM),
and Natural Language Generation (NLG).
Natural Language Processing (NLP)

4. To respond to a request as accurately as possible, the chatbot uses a combination of pattern
matches – a standard structured as Artificial Intelligence Markup Language (AIML) – to map the
text in real-time to find applicable responses.
The following are some of the main program models in the NLP mechanism:
• Tokenization
Separating a sentence into different parts, words, or “tokens” that are linguistically
representative, with a different value in the application.
• Normalization
Processing the text to discover any typographical errors and common spelling mistakes
that might alter the intended meaning of the user’s request.
• Named Entity Recognition
Searching for different categories of words or “entities” that are similar to whichever
information is provided on the site (i.e., name of a particular product).
• Sentiment Analysis
The ability to recognize users’ emotions and moods, study and learn the user’s experience,
and transfer the inquiry to a human professional when necessary.
Like most modern apps that record data, the chatbot is connected to a database that’s
updated in real-time. This database, or knowledge base, is used to feed the chatbot with
information to cross-reference and check against to give an appropriate answer to the
user’s request.
Natural Language Understanding (NLU)
NLU is the ability of the chatbot to break down and convert text into structured data for the
program to understand. Specifically, it’s all about understanding the user’s input or request
through classifying the “intent” and recognizing the “entities”.
• Intent
An action or a request the user wants to perform or information he wants to get from the
site. For example, the “intent” can be to ‘buy’ an item, ‘pay’ bills, or ‘order’ something
online, etc.
• Entity
• An idea or a concept that compliments the “intent”. For example, the user can specify
certain details such as a location, food, date, colour, size, etc.

5. For a long time, NLU only functioned on text inputs. With the advancement and growing
popularity of voice-enabled virtual assistants, however, more chatbots are accepting audio
inputs and feeding them into the NLU engine by converting speech into text
through Automatic Speech Recognition (ASR).
Dialogue Manager (DM)
After the NLU engine is done with its discovery and conclusion, the next step is handled by
the DM. This is where the actual context of the user’s dialogue is taken into consideration.
Here, it is instructive to take note of 2 sub-components that ensure how do AI chatbots
work so intelligently:
• Dialogue State Tracking (DST)
If a user has conversed with the AI chatbot before, the state and flow of the previous
conversation are maintained via DST by utilizing the previously entered “intent”.
In the case whereby the user wants to continue the previous conversation but with new
information, DST determines if the new entity value received should change existing entity
values. If the latest “intent” is to add to the existing entities with updated information, DST
also does that.
• Dialogue Policy (DP)
Once DST updates the state of the current conversation, DP determines the next best step
to help the user accomplish their desired action. Typically, DP will either ask a relevant
follow-up question, provide a suggestion or check with the user that their action is correct
before completing the task at hand.
Essentially, DP is a high-level framework that trains the chatbot to take the next step
intelligently during the conversation in order to improve the user’s satisfaction.
Natural Language Generation (NLG)
At the end of the chatbot architecture, NLG is the component where the reply is crafted
based on the DM’s output, converting structured data into text. In effect, it does the reverse
of what NLU does.
Mapped to the “intent” detected in the user’s request, the NLG will choose one of several
user-defined templates with a corresponding message for the reply. If some placeholder
values need to be filled up, those values are passed over by the DM to the NLG engine.
Finally, an appropriate message is displayed to the user and the chatbot enters a mode
where it waits for the user’s next request.

6. Are You Ready to Implement Chatbots for Your Business?
At the heart of an AI-powered chatbot lies a smart mechanism built to handle the rigorous
demands of an efficient, 24-7, and accurate customer support function. AI chatbots are
valuable for both businesses and consumers for the streamlined process described above.
With elfoBOT’s solution, you can use our chatbot platform to build AI chatbots to keep
your customers engaged in meaningful ways. Connect your users with results as quickly as
possible.