The world has witnessed significant advancements in human-computer dialogue. Today, conversational interfaces are slowly taking the place of rigid GUI dialogue boxes and web forms. The evident evolution of UI design is all about leveraging artificial intelligence to enable end-users to get answers and perform routine tasks quickly without strain. However, what’s the key to designing and building AI-powered and user-friendly chat interfaces?

In this guide, we take a look at the fundamentals of developing AI-powered conversational UI, including design principles, best practices, tricks, and effectiveness/performance tests:

Types of Conversational Interfaces


Chatbots are programs that facilitate text-based conversations between computers and people in a natural language. Developers may use AI techniques such as natural language processing to make these conversations possible by modeling human dialogue. The product is a smart chat agent that ”acts” as a human participant in two-way communication. Chatbot applications process, analyze, and ”understand” user input. They usually extract appropriate dialogue responses from content or knowledge bases.

Here are several approaches to training a typical AI chatbot to understand user input and know what response to give:

Pattern Matching: In this case, the chatbot provides an appropriate response after associating the user input with a pre-registered pattern.

For example:

User input: Who invented the telephone?

Web bot: According to Wikipedia, Alexander Graham Bell created the first technically viable telephone.

Natural Language Understanding: With NLU, a chatbot can learn and classify the user intent. For example, an intelligent support agent provides the same information, directives, options, or commands to a shopper that asks, ”Do you have a navy blue tuxedo? I want to buy one,” ”Are navy blue tuxedos available? I need one,” or ”I’m looking to buy a navy blue tuxedo.” The chatbot understands what the user is after in any of the three scenarios.

Natural Language Processing: An NLP chatbot may employ techniques such as tokenization, sentiment analysis, or normalization to decode natural language. It turns user input into structured data, which allows it to choose and give an appropriate response.

Voice-Based Assistants

People speak faster than they type or write, and voice-based bots enable them to save time when ordering items or seeking directives. E-commerce websites deploy these advanced conversational tools to provide a seamless customer experience. Here, users get their orders or questions across verbally. The smart bots respond with verbal directives or answers.

Typical conversational interface applications include:

Information/enlightenment sources: AI-powered interactive interfaces provide answers to questions that curious persons, customers, or employees ask.

Enterprise workflows: Integrating AI bots with enterprise information management systems or even IoT can help improve operational efficiencies within an organization.

Transactions: AI-driven conversational interfaces help customers perform tasks such as online ordering or booking.

Best Practices for Designing Conversational Interfaces

Always adopt an end-user-centric approach when designing a conversational interface. Here’s a list of principles to guide the design of a practical text-based or voiced-based bot:

Be as Direct and Concise as Possible

A chatbot should give straightforward answers or directives. It’s undesirable for the user to wander around an interface exploring commands or options that don’t address their concerns.

Likewise, be sure that the chat agent simplifies processes. Consider whether or not a customer may accomplish the same objective (such as book a flight) faster and more conveniently by calling a live agent instead.


What’s the location or site of your conversational interface? Can users spot it right away? You could provide hints via website pop-ups.

Also, consider deploying your informational or transactional chatbot on end-users’ most preferred communication platform or device. Take responsive design into account too, considering that many consumers prefer to shop, order, or schedule appointments via mobile devices like smartphones and tablets.

Establish a Natural Flow of Logic

Provide a procedural conversation flow that mimics the traditional user experience (UX). For example, the bot may ask a series of questions whose answers determine what the user is after. Order and structure these questions to match the logical sequence of customer concerns/inquiries in a typical shopping session. This approach helps to avoid confusing the end user.

A top-down, stepwise process would be ideal. For example, you could have a logical module that guides the user through product categories, types, brands, and, finally, specs.

Techniques for Building Smart Chat Agents

There are several approaches to creating intelligent conversational interfaces that mimic human intelligence. These include:

A Retrieval-Based Model

This type of a chatbot ”learns” to answer based on predefined questions and responses. When a user poses a problem, the bot analyzes a set of possible answers before selecting the most relevant or correct. The main advantage of a retrieval-based model is that it supplies direct, concise responses. It does not make language or grammar errors. Additionally, this type of conversational bot is comparatively less-complicated to build.

The approach has some flaws, however. For starters, it fails to take into account all possible user questions or concerns. Typically, such a conversational interface will not address issues not inherently provided for in a repository.

The good thing is that machine learning algorithms may give a retrieval-based chat agent the ability to build a knowledge base, learn, and generate new answers.

Generative Models

Generative models are advanced and capable of learning from historical user responses to generate appropriate answers. Powered by deep learning techniques, these bots build repositories of user input, which form the basis of richer engagements.

It takes a lot of interaction with users, and it requires access to large amounts of data for generative models to develop the necessary intelligence for meaningful output and coherent human-machine dialogues. Likewise, a bot created this way is prone to logical and grammatical mistakes based on the user input it receives over time.

Building an Emotionally-Aware Chatbot

An emotionally-aware chatbot responds to user concerns in a way that ”feels” and sounds caring or empathic. The bot analyzes user emotions based on their verbal or text input. It associates sentences or words to feelings like disgust, anger, fear, happiness, surprise, or neutral. It then responds in a reassuring, relieving, or comforting manner.

You can build such a conversational interface using AI sentiment analysis models and deep learning techniques. A good example is Convolution Neural Networks (CNNs). Although these were initially used to detect sentiment in images, they’ve become useful in AI text analytics.

You can also analyze sentiment using a Long Short Term Memory model, which is a type of Recurrent Neural Network (RNN). This technique preserves context, and it can generate responses based on previous states. Smart chatbots need the ability to put input into context to understand the user. They need the capability to track changing user moods in a sentence or conversation. LSTM algorithms serve this purpose well.

For instance, when a user types, ”that’s amazing,” an intelligent chatbot script feeds the text into a sentiment analysis module. The application requires accesses to a library or repository of user input. Each word in the dataset is associated with a sentiment label or value between 0 and 1.

Of course, ”amazing” is a positive feeling, to which the algorithm may assign the value 0.5867, for instance. As such, the conversational interface recognizes that the customer is happy and satisfied. In a retrieval-based model, the smart chatbot would call an appropriate predefined response, for example, ”We’re glad you like our product.”

Putting Your Conversational Interface to the Test: KPIs

It’s imperative that you develop qualitative and quantitative techniques for evaluating your AI-powered conversational interfaces. You may perform some of the tests with simulated users before deployment. However, live performance assessments are vital as they provide a more accurate picture of the system’s ability to have productive human-like dialogues. Here are several metrics and key performance indicators (KPIs) worth tracking:

Coherence of Responses

Coherence indicates the accuracy and relevance of chatbot responses. You can work it out as a percentage of correct responses out of hundreds or thousands of user interactions. If a chatbot answer does not match the expected, pre-defined answer, you may consider it to be incorrect. However, be sure to take into account reliable/actionable output that may deviate slightly from an anticipated response.

A conversational agent with weak natural language processing capabilities may have a low coherence rate. The system’s NLP algorithm is probably getting the user intent wrong most of the time.

User Sentiment

Don’t forget to build sentiment analytics into your conversational platform. If many users are reacting angrily to specific chatbot responses, maybe the system is unable to understand their needs. However, investigate deeply to distinguish between negative reactions triggered by bot weaknesses and customer disappointment due to product/service-related issues.


A user is likely to hang around for longer and probe further in a highly-engaging human-machine conversation. In that case, the chatbot is probably doing a great job. On the other hand, most users will quickly abandon meaningless or boring dialogue.

Consider the average number of follow-up turns and conversation length when measuring engagement. Take note of extended human-machine dialogues where users keep asking the same questions in modified forms.

Response Time

The time interval between user input and machine response matters too. Several factors fall into play here, including server capabilities, load times, and output processing at the user side. Response time impacts the user perception of the chatbot’s capacity.

Topical Diversity

An informative chat agent should have the capacity for a broad range of topics and vocabulary. If it keeps giving the same answers to different, unrelated questions, it probably lacks topical diversity. It has a shallow knowledge base if it returns ”I don’t know” responses a significant number of times.

Depending on the intended application, the best AI chatbot is versatile, and it can narrow conversations down to individuals, personalities, events, or places. It should have accurate responses to a broad spectrum of issues, from politics and science to sports.

In Conclusion

Conversational interfaces have helped revolutionize the user experience significantly. These smart text-based or video-based chat agents facilitate human-computer dialogues, driven by AI techniques like NLP. Hopefully, reading this guide helps you figure out where to start when building an intelligent chatbot of your own!