How to Make an AI Chatbot: A Comprehensive Step-by-Step Guide

Step 1: Define Your Scope

What are you building this AI chatbot for?

Every project needs to start with a clear purpose. By determining the purpose, you and your team can evaluate the deployment channel, suitable tech stack, customized greeting, and the list goes on. Sit down and brainstorm the most common chatbot use cases within your organization and write down specific examples. The following are common AI chatbot use cases:

• Customer service: Chatbots assist by answering FAQs or troubleshooting problems. The team can completely automate customer service processes or have the bot only augment part of the customer service team.
• Lead generation and qualification. In this case, the bot asks customer questions to capture contact information and qualify leads for sales teams.
• E-commerce assistance: If users have trouble finding specific products, the chatbot helps users locate them, check availability, and checkout.
• Book appointments: Chatbots can ask questions, issue reminders, and confirm messages to fully automate scheduling chores.
• Survey and feedback collection: After a transaction or service, the chatbot can ask questions to collect feedback.

Even though you may have several use cases in mind, try to determine a key feature of the chatbot.

Step 2: Select a Platform

Keep your users and prospects front and center during every development step, including this one. Which platform are they most familiar or comfortable with? Most organizations choose to integrate the chatbot with their websites – companies use popular website providers such as Shopify or WordPress. However, you can also integrate the bot into standalone mobile apps and social media channels like Facebook Messenger, Whatsapp, etc. It’s also possible to integrate the bot into platforms or messaging tools already available in the tech stack (for example, Slack).

To sum it up, selecting a platform requires one to consider:

• User’s ease of use and accessibility.
• The multi-channel strategy and whether the strategy aligns with the business’ overall business goals. In this case, use the same chatbot stack across multiple channels, instead of Rasa for your website and a separate bot powered by Dialogflow for your mobile app. Unify the architecture to ensure a smooth and consistent customer experience.

Step 3: Select a Tech Stack

Once you nail down the purpose and main deployment channels, it’s time to move on to the tech stack and choose suitable technologies.

Depending on the initial goal, you can utilize pre-trained AI models or custom-build your chatbot from scratch.

• BERT, GPT-2, and ELMo are famous pre-trained AI models. They’re a great choice because you can take advantage of their learned pattern, reduce computational burden, and economize resources. These models are also a great starting point for chatbot development, as they can be fine-tuned to meet specific requirements or results.
• If standard pre-trained models don’t meet your unique business needs, a custom AI chatbot would require numerous integrations and a team with extensive knowledge in Python machine-learning libraries and frameworks like TensorFlow, PyTorch, or Scikit-learn. These tools allow developers to create chatbots tailored to your exact requirements, offering flexibility and advanced capabilities that standard chatbot engines can’t provide.

In this step, we will discuss in depth the main aspects and tools involved in building a custom AI chatbot from scratch.

Natural Language Processing Tools

NLP is a branch of AI that enables computers to comprehend, interpret, and respond to human language in text or speech.

To understand and respond to human language, an NLP goes through a sequence of steps:

• Normalizing: First, the chatbot cleans up the message by removing irrelevant details like capital letters or extra spaces.
• Tokenizing: The bot breaks up the message into smaller parts – tokens, like words or phrases, and removes punctuation. For instance, “I need help.” becomes [“I”, “need”, “help”].
• Intent classification: After cleaning and filtering the text, the bot figures out what you’re trying to say or ask. For instance, it identifies if you’re asking for shipping information or trying to place an order.
• Recognizing entities (optional): This optional step is when the bot asks for more specific details regarding the message, e.g. order number or transaction for a more accurate reply.
• Response generation: Using advanced AI, the bot generates a list of possible replies and chooses the most suitable one to send to the user.

Python is well-known for its extensive support for NLP tasks, primarily through the Natural Language Toolkit (NLTK), an open-source library. DialogFlow, Lyro, Amazon Lex and IBM Watson Assistant are other popular NLP tools to create chatbots.

Cloud Infrastructure

Cloud deployment allows you to scale and manage your AI chatbot model while reducing the cost and complexity of managing on-premise infrastructure.

There are many cloud platform options one can choose from, such as Google Cloud Platform (GCP), Microsoft Azure, Amazon Web Services (AWS), IBM Cloud, etc. Each comes with its own pros and cons, depending on your demands and budget. Make sure to do your homework before choosing one, as each platform offers different AI frameworks and libraries, billing models, and security and compliance standards for encryption, authorization, and auditing.

Machine Learning Libraries

AI chatbot creation and operation rely a lot on machine learning (ML). With ML, chatbots can learn and improve their responses with time. To be specific, ML algorithms analyze the user’s input, guess the user’s intentions and generate responses. This continuous learning cycle personalizes user experience and makes chatbots more engaging.

There are three common ML libraries developers typically go for:

• TensorFlow: Tensorflow is an open-source ML framework developed by Google. It offers a variety of tools to support neural network training, chatbot deployment strategies, and building and training models. The framework boasts a large and supportive community.
• PyTorch: Developed by Facebook, this open-source framework is known for its ease of use. It’s favored in the research and academic community for its intuitive user interface.
• Scikit-learn: Scikit-learn is a popular open-source ML for Python. It’s a valuable tool for mining, data analysis, and machine learning, as well as fine-tuning chatbots.

Chatbot Development Platform

To simplify the development process, the following chatbot development platform offers a wealth of tools like NLP, ML capabilities, and API integrations.

• Dialogflow, powered by Google, is a platform for creating conversational interfaces for websites, apps, messaging platforms, and IoT devices. It leverages Google’s ML for accurate intent recognition and context handling, offering advanced NLP and NLU capabilities. Key features include seamless integration with platforms like Google Assistant and Slack, support for responses using texts or mages, and multi-language functionality for a global audience.
• Microsoft Bot Framework: This platform enables the creation of intelligent bots that interact naturally across multiple channels, offering tools like Bot Builder SDK and Azure Bot Service. It supports integration with platforms like Skype and Microsoft Teams, utilizes AI and cognitive services for language and sentiment analysis, and ensures scalability via Azure.
• Rasa is an open-source platform for creating contextual AI assistants with full control over data and functionality, deployable on-premises or in the cloud. It offers advanced NLP, dialogue management for dynamic conversations, customizable integrations, and strong community support.

Step 4: Build Your Chatbot

After sorting out all the technical details, it’s time to feed knowledge and build a personality for your chatbot.

Build a “Personality”

With your target users in mind, what kind of “representative” will the chatbot be like? What kind of “personality” does the bot present? In other words, will the bot use a casual tone or a more formal one? Do the responses come with industry slang or on-brand emojis? Do the responses come with images or gifs? Craft a greeting that’s fitting with all the traits you listed. We have a few tips for the first greeting so you can nail that first impression:

• Establish the bot’s purpose during the greeting.
• Personalize the greeting by programming the bot to extract values like “first name” for customers who already logged in.
• Include a prompt to ask for user’s information, like what they need help with or what information they’re looking for.

Training Your AI Chatbot

AI chatbots need training to build up their knowledge base. This involves training the AI with plenty of relevant and high-quality data. This data can come from a number of sources.

• Internal data: Gather data from internal sources like support tickets, social media, reviews, and documentation.
• Public datasets: You can find numerous online datasets customized for different industries. For example, the Stanford Sentiment Treebank (SST) is a labeled dataset for sentiment analysis that provides fine-grained sentiment labels for movie reviews. It’s widely used to train and test sentiment classification models.

Step 5: Design Chatbot Conversation

In chatbot builders, you can create conversation flows by dragging and dropping elements. However, building chatbots from scratch means you need to put in work to configure the decision tree. A chatbot decision tree is a flowchart-like diagram that guides users through various decision paths by presenting a series of questions. Named for their tree-like appearance, these diagrams resemble an inverted tree, with branches splitting into smaller branches that conclude at leaf nodes.

Decision trees provide a structured approach to managing interactions between the bot and the user. Based on the input and answers, the bot can guide users to their required information or actions. However, the chatbot still needs training to take on such a proactive role.

One aspect involved in such training is prompt engineering. Prompt engineering involves crafting and refining instructions or queries (prompts) to guide AI models, especially large language models (LLMs), in generating desired outputs. It focuses on natural language processing and requires skills in language, context, and effective communication with AI. You need to choose a base LLM like GPT, BERT, or T5 to execute this task.

Step 6: Integrate and Test Your Chatbot

Integration

There are several ways to integrate your chatbot into the website or application. You can use API integration or data synchronization to integrate with the existing infrastructure. For user interface integration, embedding a chatbot in mobile or web applications allows it to be easily accessible through a floating chat icon or a dedicated section within the app.

Testing

Like any other software product, you want to thoroughly test it before launching. Test every possible variation of your chatbot flow – this might seem tedious, but it’s well worth it to ensure the most seamless user experience.

Test to see if the bot understands queries and provides accurate responses. Using automated testing scripts helps you test as many cases as possible. Don’t forget to test user loads and see how the chatbot behaves during peak usage time.

Last but not least, do rigorous security testing and double-check if the security measures are implemented in place and follow the basic standards.

Step 7: Launch, Measure, and Optimize

Launching a product is exciting, but that doesn’t mean the hard work is over once you reach this stage. To maintain a product’s quality, you need to constantly monitor its performance. When the chatbot is live and in use, measure and take note of its performance to make timely improvements.

• Monitor the chatbot’s activity. How often is it used?
• Bounce rate: The percentage/rate of visitors that land on a page of your website, don’t interact, and then go is known as the bounce rate. If the bounce rate is high, try to determine the potential bottleneck or problem.
• Goal completion: Measure how often the bot completes its goal or meets the user’s requirement.
• How often do your users need to contact customer service for help using the chatbot? If the rate is too high, then you need to retrace and see what causes users to get stuck.

Don’t forget to collect feedback from users. For example, you can ask users to rate how satisfied they are with the bot or to leave their thoughts about the bot. Combined with all the information gathered from users, continuously finetune and update the training data for your bot.