How to Build an AI Chatbot in 7 Steps [2025]

So, you wanna build an AI chatbot?

I've built my fair share of chatbots — and our company has helped deploy over 750,000 bots, if you can believe it — so you've come to the right place.

These days, anyone can build an AI chatbot, no matter their level of technical expertise.

Whether you want to build a chatbot for customer service, an AI sales bot, or something just for fun, we have you covered.

1. Define your scope

The first step to create an AI chatbot is simple – its purpose. What’s your chatbot going to accomplish?

You might be quick to say 'answer common customer questions,' or 'recommend products'. Perfect. You have the broad strokes settled.

• But what tools does it need to connect to? Does it need to connect to a CRM like HubSpot?
• What kind of Knowledge Base does it need to give accurate information? Will it be drawing from an LLM's knowledge, or will it just use the information you choose to give it?
• And finally: where will you deploy it? On your website? On WhatsApp or Facebook Messenger? Maybe it's an internal workplace bot and you want to use it via Slack or Microsoft Teams.

These questions will determine what capabilities your chatbot will need, which will determine the platform you use. 

2. Choose a chatbot platform

With the explosion of AI companies in the past few years, there are plenty of AI chatbot platforms for you to choose from.

There's really something for everyone these days:

• Need an open-source chatbot platform? No problem.
• Want something more advanced? Try investigating the array of AI agent frameworks at your disposal.
• If you don't want another company's logo plastered on your chatbot, look for white label chatbot platforms.

I won’t dive into the pros and cons of each platform – since I'm a little biased on ours – but I’ll give you a few considerations to keep in mind when selecting one for your project.

Make sure you pick a platform that:

• Has a broad swath of educational resources. There’s always going to be a learning curve, so ensure you’re well-equipped for it.
• Matches your intent. Don’t pick a platform that specializes in customer service if you want a chatbot for both customer support and sales.
• Includes a free tier, so you can test it out before (or without) making a financial commitment.

Once you pick a platform to start off with, you can head straight to the fun part: the build. 

3. Build your AI chatbot

There are a lot of ways to build an AI chatbot, but here's one. It uses our platform, and it leads to a customer service, AI lead generation, or product recommendation bot.

Before you start building, you should get a rough draft of your basic chatbot design — the user journey, some conversation design, and all the possible results of the chatbot interaction. Can users be handed off to a human? Can they be emailed a case study afterwards?

Once you've mapped out the basics, it's onto the fun part: building your chatbot.

Step 1: Set Up Your Chatbot Project

Before you start designing conversations, you need to define the chatbot’s purpose and configure its behavior. This ensures the chatbot aligns with your business goals and delivers the right user experience.

a) Create a new project in Botpress and select "Start from Scratch" for full customization.

b) Define the chatbot’s purpose and tone of voice.

• Is the chatbot providing customer support, assisting with sales, or offering general site navigation?
• Set Agent Instructions to dictate how the bot should interact with users.

This step is crucial because it sets the foundation for how the chatbot will function. A well-defined chatbot behaves more predictably and ensures a smooth user experience.

Step 2: Add Your Website & Documents Knowledge Bases

A chatbot is only as smart as the information it has access to. Instead of manually programming responses to every question, Botpress lets you import external knowledge sources so the chatbot can generate accurate answers dynamically.

• ‍Go to the "Knowledge Base" section in Botpress.‍
• Upload your website link to allow the chatbot to reference specific pages for information.‍
• Add documents as txt or pdf files to add further information specific to your pitch deck or FAQs.

This ensures the chatbot delivers accurate answers by pulling from FAQs or service pages, eliminating manual updates.

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Step 3: Create Tables to store captured information

If your chatbot needs to capture leads or store data, Botpress Tables collect structured details like names, emails, and inquiry types. This is how you bring them into your workflow.

• ‍Go to "Tables" in the Botpress menu.‍
• Create a new table (e.g., "customerLeads").‍
• Define the columns based on the data you need to store:‍
  
  • Name (to personalize responses)‍
  • Email (for follow-ups)‍
  • Company Name (to tailor suggestions)‍
  • Inquiry Type (to categorize requests)

Without this step, any data the chatbot collects is temporary and cannot be referenced later. Storing it properly allows for follow-ups, analytics, and workflow automation.

Step 4: Build the Workflow

Once we have declared the knowledge sources, we can go ahead and integrate this knowledge with LLMs to create an workflow for our chatbot.

Craft your starting message

While a chatbot powered by an LLM (as most are these days) can handle conversation, you should customize your greeting for the best user experince.

Your greeting should be as clear as possible: be up front about what your chatbot can help with.

A few other tidbits of advice for chatbot scripting:

• If you want to make it super easy for your users, you can opt for chatbot buttons that allow users to select their question or issue without having to type it out.
• Try to make your chatbot sound more human than cold and robotic. (But clarity over humor, every time.)

Use plain language instructions to response to queries

Using an Autonomous Node, you can use plain language instructions to explain how your chatbot should handle questions.

For example, you can instruct your bot to "Direct the user to make a Calendly appointment if their budget is over $5000."

This way, your bot has the freedom to respond to questions in a natural manner, but has rules and guidelines about how to handle encounters.

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4. Integrate tools and knowledge

If your chatbot is going to be using any tools or platforms, you'll have to connect them (or pick a chatbot platform with a library of pre-built integrations). And if it needs any company knowledge (like a website, databases, product inventory, etc.), then you'll definitely need some Knowledge Bases.

Connecting tools

The most common integrations we see (and we see a lot of them) are for CRMs. You can connect a chatbot to the HubSpot CRM, a repository like GitHub, planning tools like Asana and Notion, scheduling tools like Calendly. . . the list is endless.

The process to connect tools will, of course, change wildly based on the tool or platform you want. If tinkering isn't your forte, I'd suggest picking a chatbot platform that offers a pre-built integration for your necessary tools.

Adding Knowledge Bases

A Knowledge Base can be a table, document, or website that includes the information your AI chatbot will draw from. 

Common types of Knowledge Bases include company websites, product inventories, FAQ documents, and internal databases.  

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For example, an HR chatbot will use a company’s key policy documents as a Knowledge Base. When an employee asks how to handle a specific situation, the chatbot can refer to the policy documents (using  retrieval-augmented generation) to inform its answer.  

A note on RAG

The best way to get accurate responses from a Knowledge Base or any file type is to use a chatbot platform with retrieval-augmented generation (RAG). Responses that use RAG won't generate random information – they'll always base their answer in the Knowledge Base you provide.

RAG is increasingly seen as the standard in professional chatbot design because it allows AI to provide grounded, source-based responses.

5. Test and iterate

Once you’re done building your AI chatbot, it’s time to make it better. Some builders forget to account for testing and iteration time, but this is a crucial step to deploying a successful chatbot.

Whichever AI chatbot platform you choose, it should have a simulator within the studio that allows you to practice conversations with your chatbot. This is the first step of testing that you’ll use throughout the building process.

Once your build is complete, you’ll be able to send a sample version of your AI chatbot to your friends or colleagues using a URL. You should do this to test out your bot’s functionality before officially deploying it.

As you test, you’ll be able to tweak your bot for the better. And be prepared: this process will continue even after you deploy your chatbot. It’s normal. 

6. Deploy

Where is your chatbot going to live? The most common deployment options are 1) company websites, and 2) WhatsApp channels.

Deploying a chatbot will require connecting it to your chosen platform and configuring permissions, integrations, and triggers. This process will differ depending on the place of deployment you choose.

But if you choose a popular platform, we can help you out. My talented coworkers and I have created step-by-step tutorials for:

• Chatbots for websites
• SMS chatbots
• WhatsApp chatbots
• Telegram chatbots
• Facebook Messenger chatbots
• Instagram chatbots
• WordPress chatbots
• Wix chatbots
• Slack bots

If you opt for a classic website bot, you can choose whether a traditional widget is best, or a format like a dedicated webpage (you can check out an example from one of our customers here).

And don’t forget to communicate to your users that your chatbot is live – if they don’t know it’s there, then your AI chatbot isn’t able to properly fulfill its purpose.

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7. Monitor

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Your AI chatbot project doesn’t end after deployment – in fact, deployment is just the beginning. Once it’s out in the world, your AI chatbot starts working for you. 

Any AI chatbot platform worth its salt will provide you with ongoing analytics about your chatbot – when people are using it, the topics they ask about, and the platforms people are using to engage with it.

In fact, chatbot analytics should be at the heart of your post-deployment strategy. You can't properly reiterate on your bot if you're not clear on areas for improvement.

If you want to better understand how to manage and improve your chatbot after deployment, you can check out our free course on Managing your Chatbot. 

Start building a free AI chatbot today

You have an idea for an AI chatbot – and we have the most advanced, user-friendly platform available.

It’s easy to build on Botpress with a drag-and-drop visual flow builder, extensive educational library, and an active Discord community of 25,000+ bot builders.  

Our extensible platform means you can build anything, and our Integration Hub is full of pre-built connectors to the biggest channels.

‍Start building today. It’s free.

FAQs

1. Can I migrate my existing chatbot (built on another platform) to Botpress?

Yes, you can migrate your existing chatbot to Botpress, but there is no automated import tool. You'll need to manually rebuild your flows and integrations using Botpress Studio to replicate your original bot's functionality.

2. How do I determine if my business really needs an AI chatbot?

To determine if your business needs an AI chatbot, assess whether your team spends significant time on repetitive inquiries, customer support, or lead qualification. If automating these interactions could reduce costs or scale engagement without hiring more staff, a chatbot is a good investment.

3. How frequently should I retrain or update my chatbot?

You should retrain or update your chatbot quarterly, or whenever there are significant changes to your product offerings or FAQs. For high-traffic bots, monthly performance reviews and incremental updates based on analytics (like misunderstood intents or drop-offs) ensure optimal accuracy and engagement.

4. What are some common pitfalls to avoid when launching your first AI chatbot?

A common pitfall when launching your first AI chatbot is trying to cover too many use cases before validating core functionality. Avoid overengineering; instead, focus on a minimal viable flow, gather feedback, and iterate based on real-world performance before scaling.

5. How do I run A/B tests on different conversation flows?

To run A/B tests on chatbot conversation flows, clone your flow variants and use logic or a random assignment method to split users between them. Track outcome metrics such as task completion rate or conversion to determine which version performs better, and iterate accordingly.