Build an AI Chatbot without Coding: A How to Guide

The releases of Microsoft-backed ChatGPT, Anthropic’s Claude, and Google’s Apprentice Bard have made the world realize, myself included, that Large Language Models (LLMs) are bringing about a revolution comparable to that of the Internet itself. Even in their earliest stages, these newfound capabilities represent a paradigm shift in how businesses can use technology to complement user experience and user interaction to streamline operations and drive revenue without losing human touch. With it, the concepts of what AI can accomplish and how businesses approach building an AI chatbot have changed forever.

The defining feature of LLM-based intelligent chatbots will be the automation of many tasks that—up until now—were only successfully executed by humans. Before ChatGPT, no conversation system in existence could compete with the performance of a human mind in terms of natural language understanding and generation. A chatbot could only handle structured and scripted conversations, relying on human intervention whenever the end user went off script. LLMs closed the chapter to this era. Despite the limitations on groundedness, factual knowledge, inevitable hallucinations, etc., it’s difficult not to be impressed by its capabilities. And why is that? It’s because ChatGPT has anthropomorphic interactions with people. In other words, human-like conversations with AI chatbots are a tangible reality—a reality that Landbot team managed to make accessible to all. Thanks to the LLM-driven features, we moved from helping you build simple rule-based chatbots to enabling you to build an AI chatbot capable of human-like conversation flow—without coding.

Where does that leave us amid this new AI chatbot ecosystem? Without further ado, I’d like to introduce you all to Landbot AI—but let’s cover some bases first.

Understanding AI: What is NLP/NLU/NLG? What is ML? What are LLMs? What connects and differentiates them?

In the dynamic realm of tech vernacular, AI, NLP, and ML are frequently tossed around interchangeably, but a nuanced hierarchy shapes their interplay beneath the surface.

Positioned within this framework, Natural Language Processing (NLP) assumes the role of a subset, nestled beneath the broader expanse of Machine Learning (ML), both operating within the overarching ambit of Artificial Intelligence (AI):

• AI: Originating in the 1960s, AI stands as a cornerstone subfield in computer science, devoted to imbuing machines with the capability to grapple with challenges inherent to human cognition. Conceived as an entity mirroring human capabilities, an ideal AI would transcend physical and intellectual boundaries, undertaking tasks spanning navigation, planning, stimulus recognition, linguistic communication, and even the realms of artistic creation. However, the realization of this utopian vision remains a distant horizon.
• NLP: NLP, a linguistically-inclined discipline within computer science, focuses on endowing software with the insight to comprehend natural human language, whether in written or spoken form. It comprises two sub-disciplines: natural language generation (NLG) and natural language understanding (NLU).
• ML: Conversely, ML represents a targeted initiative to craft software systems capable of autonomous learning from their own observations and past experiences. In essence, machine learning empowers NLP algorithms to evolve autonomously by assimilating knowledge from each new interaction, thereby elevating their performance through a continuous cycle of self-improvement.

Given the multifaceted nature of linguistic inputs and diverse scenarios inherent in natural language, the expectation of a single developer programming for every conceivable case is impractical. Thus, the efficacy of natural language processing in AI is contingent upon the symbiotic support of machine learning...

That was the status quo until LLMs steamrolled through this fragile ecosystem.

• LLM: Large Language Models are AI systems that have been trained on massive amounts of textual data: books, web pages, manually curated examples, as well as software code. Their training paradigm is simple. They adjust their model parameters so that they accurately predict the next token in a sequence (words, code statements, etc.) The discovery of this decade is that with ample enough models and data, LLMs can accomplish more complex tasks: answering questions, summarization of texts, language translation, as well as few-shot learning—the models can learn to perform tasks correctly with fewer tries. Given a prompt, the AI is able to complete the task at hand. This means LLMs can generate text similar to how humans speak and write.

So what’s the major hype about ChatGPT?

ChatGPT has demonstrated that a single LLM, with minor customization, can eliminate the need to train Natural Language Understanding (NLU) and Natural Language Generation (NLG) models. You only need to give the LLM a prompt explaining what you want it to do, and the AI will do it for you. Apart from the fact that ChatGPT has been trained with massive data sources, its ability to interpret a request, tailor content according to that request, and “remember” what was said earlier in the conversation makes it ripe with potential.

Limitations of What are the Possible Limitations of LLMs Today?

When LLMs barged onto the playing field, they did not come without caveats. Social media was exploding with new applications being built on top of GPT-like models. With all the noise, it was easy to forget that most of these products and applications were just proofs of concept—not fully functional products that could be sold or offered at scale. Below are some of the main limitations that came to light:‍

• Lack of groundedness: Trained on text data alone, an LLM has no way of understanding what the symbol “cat” in a sentence actually means. It only knows that “cats” are related to other words such as “meowing” or “hissing”. Nevertheless, this limitation is improving with the use of more datasets like description-code pairs or instruction datasets.
• Hallucinations: Yes, you read that right! At times, LLMs can generate complete nonsense. The output has the right “form,” but the content makes no sense. This tends to happen when the model doesn’t have access to the information it needs to answer the query, so it makes them up!

• High latency times: LLMs take their time to generate responses. This means LLMs are not suitable for applications or products that need fast response times (less than a few seconds).
• Expensive to train and deploy: LLMs need to be deployed on specialized, expensive hardware, even to meet the slower output speeds they are challenged by today. They are also trained on terabytes of data. The estimated cost of training a billion-parameter LLM model is around one million dollars.

All of the above concerns are true; However, within a span of a single year, significant improvements were made in each of these areas. We have already witnessed a huge improvement in terms of training datasets since the first release of GPT models just within the span of the past year. LLM hallucinations can be controlled with the right prompt strategy. The latency issues have been significantly minimized, as well. Plus, low-code and no-code features (like those I'll introduce in this article) sprouted like mushrooms after rain, offering cheaper and more accessible ways to deploy LLM solutions.

So, how do you build an AI chatbot from scratch, and how does Landbot AI manage to make it code-free? I’ll tell you now.

How to Build an AI Chatbot without Coding

We’ve always dedicated our efforts to helping businesses and their operational managers overcome the challenges they’ve faced in the past when it comes to conversational automation, regardless of the types of chatbots they employed. However, with the rise of LLMs, we turned our focus on developing reliable and easily applicable solutions built on top of the deep learning model.

Here is your step-by-step guide!

1. AI or No AI: Don’t Forget the Essentials‍

Even with no-code solutions in the AI chatbot market, designing the proper user experience that aligns with your brand, building the bot in collaboration with peers, and deploying it at scale are all essential to the AI chatbot-building process. These are the fundamental needs when it comes to chatbot building that is unlikely to change even as more AI-powered chatbot solutions become available. From our perspective, these are:

• Designing task-specific conversational experiences: No matter where your customer is on the journey, businesses will still need to think specifically about the experience they are creating for end users. AI-powered chatbots do not change the need to design frictionless experiences that alleviate pain points for customers to acquire, nurture, and retain them successfully.
• Optimizing chatbot flows based on user behavior: AI chatbots become more intelligent over time. Continuous learning is part of their draw in the market we are seeing right now. Nevertheless, companies still need to analyze the performance of the bot and optimize parts of the flow where conversion rates may drop off based on how users interact with the chatbot. AI or no AI.
• Integrating seamlessly with third parties: Building AI chatbot solutions does not remove the need for easy integration with third-party platforms. Regardless of the data captured by the bot, it’s what happens with that information that matters. The chatbot will still need to create, retrieve, and update the data obtained from conversations properly in the tech stacks/CRMs used by your teams. Seamless integration still matters.
• Providing chatbot assistance on different channels: Chatbots can and should be deployed across the different channels your customers use: WhatsApp, website, Messenger, etc. Using AI does not negate the fundamental need to meet your customers where they are, on a user interface where they feel comfortable—and engage them through friendly conversations.

2. Clarify Your Use Cane & Start Training Your Bot

Landbot's LLM-based AI solutions that allow you to emulate human conversations consist of three key use-case-focused features: Customer Service, Lead Generation, and Appointment Assistants. All of the solutions are fully automated, making building AI chatbots simpler than ever. So much so that you can build your own using a simple configuration without even accessing our usual no-code builder.

Note that all of the solutions are available for Web and WhatsApp. Once you pick your use case, you'll be asked to select your preferred channel.

Let's have a look!

Customer Service (FAQ Assistant)

This feature is not just a customer support hero but a sales ally, too. It's not just about enhancing their experience; It's about fostering trust and excitement, upping the odds that your prospect will convert. The setup? You can be done in minutes. All you need to do is copy-paste your FAQ data—be that in a Q&A format or a concise text—and feed it to the bot.

Once that's done, you can go ahead and test it on the right side of the screen. And that is it. The AI will use the information you submitted to respond to common questions "in its own words" (staying faithful to the facts but using linguistic variations like a human would.

This feature is available, as shown, outside the builder, as a simple chatbot solely focused on answering FAQs. Or, if you want to use it as a part of a more complex assistant, you can integrate a block equivalent inside the builder.

Learn more about the AI FAQ Assistant here!

Lead Generation Assitant

If your chatbot's primary objective revolves around extracting information from incoming leads, this solution is just the ticket. Again, once you select this option and, subsequently, the preferred bot channel, you will be redirected to this simple editor:

The configuration might take a few extra minutes as opposed to the FAQ assistant, but the simplicity remains. All you need to do is customize your welcome message and then proceed to define the questions you want the bot to ask your prospect and the variables under which you want to save the corresponding user inputs and answers. The bot will take these questions as instructions. In the conversation, it will proceed to ask the user for this information in its own words and then save them under prescribed variables. The conversation will only conclude once the bot has gathered all of the data required.

If you are pressed for time or simply want to explore, you can click on the "Use Example" buttons:

Each of these options starts you off with a case-specific set of questions.

Beyond that, you can access the "Assistants Settings" section and take your optimization further.

This section allows you to further improve the nuances of your. It provides the GPT model with more information and guidelines about interacting with your leads and accurately representing your company. As background information, you can feed it information about your company mission, offering, common questions and answers, and more.

Once again, you can use this feature as described above or as a single block within a larger bot inside the Landbot no-code builder. Learn more about Lead Generation Sales Assitant here!

Appointments (AI Appointment Assistant)

Last but not least, there's the AI Appointment Assistant that makes scheduling meetings a piece of cake for both parties involved. Meticulously crafted, this advanced AI tool aims to simplify the appointment scheduling process, providing unparalleled convenience not only in its application but also in its usage.

To configure the bot, simply work your way through these six sections step-by-step:

• Your Calendar: Connect your Google account. Customize the meeting by clarifying available dates and time slots, meeting duration, etc. 
• Ask the Lead: Tailor the experience by selecting the questions you want the lead to answer as part of the pre-meeting discovery. Standard questions are provided, but feel free to add custom ones,
• Answer the Lead: Feed your bot the most pertinent information a lead is likely to ask about in the pre-meeting stage so the bot can swiftly resolve their doubts.
• Customize Assistant: Give your Assistant a name, choose languages for interaction, and give it a bot of personality.
• Notification: Wrap it all up by providing an email to receive meeting notifications and information gathered by your AI Assistant.
• Preview: Text your creation!

And that is all. It's really that simple. Feel free to try the AI Appointments assistant now for Web or for WhatsApp or learn more about here.

Not Quite Sure? Book a Demo!

If you are not quite sure about which use case to go for or would like to combine the features to create a more complex assistant, talk to us! We can help you clarify your needs as well as assist in building a more sophisticated chatbot that combines different use cases and even connects with your CRM. 

Building AI Chatbots: To AInfinity and BOTyond

Whether it’s to generate leads, launch promotional campaigns, automate processes, or provide quality customer service, our mission has been to help businesses build frictionless conversational experiences from end-to-end. The vision for our no-code chatbot builder is based on turning conversations into profitable outcomes, tripling efficiency through automation, and cutting operating costs. Regardless of the channel—WhatsApp, website, or Messenger—our goal has always been to enable anyone to create automated chatbot flows that better engage customers no matter where they are on the journey. We believe the core of any good business is based on relationships. And relationships are built on top of conversations with customers. 

ChatGPT and other LLMs have only set the bar higher in terms of consumer expectations for the conversations they have with brands. And this new technology has added fuel to the fire for us Landbotters. With it, we can help our clients build AI chatbots with GPT or other tools more efficiently by reducing development and deployment times—without sacrificing the experience. 

The future of conversational automation is here. Now the time has come to invite those of you in our community to join us in the AI chatbot revolution. I am pleased to announce that Landbot AI is officially launched! Are you ready to start building AI chatbots with Landbot AI?