Building Conversational AI: Techniques and Examples

Conversational AI, also known as chatbot or virtual assistant, is a technology that enables machines to understand and communicate with humans in natural language. It has become increasingly popular in recent years due to its ability to automate customer service, provide personalized experiences, and increase efficiency in various industries.

One of the most common examples of conversational AI is the chatbots that we encounter on websites, social media platforms, and messaging apps. These chatbots can help us book flights, schedule appointments, order food, and answer our questions. They are programmed to understand our queries and respond accordingly, providing us with quick and (hopefully) accurate answers.

But how do we build conversational AI? There are several techniques that developers use to create chatbots and virtual assistants.

Natural Language Processing (NLP)

The first and most important technique is Natural Language Processing (NLP). NLP is a subfield of artificial intelligence that focuses on understanding human language. It involves breaking down sentences and phrases into their individual components, such as nouns, verbs, adjectives, and adverbs. Then, using machine learning algorithms, it analyzes the meaning behind the words and constructs an appropriate response.

For example, if someone asks a chatbot “What is the weather like today?”, the NLP algorithm would recognize the question as being related to weather and provide a response such as “Today’s forecast is sunny with a high of 80 degrees.”

Machine Learning (ML)

Another important technique in building conversational AI is machine learning. Machine learning is a subset of AI that enables machines to learn from data without being explicitly programmed. It involves feeding the chatbot with large amounts of data, including text, voice, and images, to enable it to learn and improve its responses over time.

For example, a chatbot that provides customer service for an e-commerce website could be trained on a large dataset of customer inquiries and their corresponding responses. The machine learning algorithm would analyze the data and identify patterns, enabling the chatbot to provide accurate and relevant responses to similar inquiries in the future.

Natural Language Generation (NLG)

Natural Language Generation (NLG) is another technique used in conversational AI. NLG is the process of converting structured data into human language. It is often used to generate automated reports, product descriptions, and other types of content.

For example, a chatbot that provides stock market updates could be programmed to generate a daily report summarizing the day’s stock market trends. The chatbot would use NLG to convert the structured data into a human-readable format that is easy to understand.

This, in my experience is the hardest thing to do.

Intent Recognition

Intent recognition is the process of identifying the user’s intention behind their query. It involves analyzing the user’s words and phrases to determine what they are looking for.

For example, if a user asks a chatbot “How much does it cost?”, the intent recognition algorithm would recognize that the user is asking for pricing information and respond accordingly.

Context Awareness

Context awareness is the ability of a chatbot to understand the context of the conversation. It involves analyzing previous interactions with the user to provide more personalized and relevant responses.

For example, if a user asks a chatbot “What time does the store close?”, and then follows up with “What time does it open?”, the context-aware chatbot would recognize that the user is asking about the store’s hours and provide the appropriate responses.

I’ve been playing around and building these for a while. Mostly in Slack since the mechanism is already there for delivery. It’s so much fun to build these services that can provide value quickly. All of these require tremendous amounts of data to be successful and relevant.

Building conversational AI applications is a crucial part of developing efficient applications. These are just some examples of the ways you can implement it.