How to Build a Custom GenAI Chatbot with Code

Picture this: Your customers can get personalized support and product recommendations instantly, at any time of the day, while your team focuses on more complex tasks.

In today's competitive digital world, artificial intelligence is revolutionizing the way businesses interact with customers, making tools like personalized AI chatbots essential to increasing efficiency and customer satisfaction.

In this post, we'll explore how to build a chatbot that's intelligent, scalable, and perfectly aligned with your business needs, even if you're new to AI.

Why are personalized AI chatbots important for your business?

A digital assistant can be able to understand your industry-specific language, interact with your customers in a natural and engaging way, and be available 24/7. AI chatbots can handle everything from answering common customer questions to providing personalized product recommendations.

With a custom solution, you can seamlessly connect your AI chatbot to your existing IT infrastructure and automate tasks in a way that off-the-shelf products can’t. Here are some common use cases for custom AI chatbots:

Customer support (post-sale)
Sales support (pre-sale)
Task execution
Information retrieval

By building a chatbot tailored to your needs, you can significantly improve both the customer experience and operational efficiency.

Choosing the right technology stack

To build a truly custom AI chatbot, it is essential to develop it with code. This allows you to extend its functionality and integrate it deeply into your IT systems.

As languages and frameworks, we recommend C# and Semantic Kernel, especially for companies using Microsoft tools.

C# is widely used in enterprise environments and its construction ensures that the chatbot can seamlessly connect to existing systems such as CRM, databases, and cloud infrastructure.

The Building Blocks of a Custom AI Chatbot

There are several key components that come into play when building a custom AI chatbot. Let’s break them down step by step.

1. The LLM Model

The foundation of any AI chatbot is the Large Language Model (LLM). There are two main options:

Private Models: Hosted on your own servers or a private cloud, they offer more control and customization. Ideal for companies that handle sensitive data but need more resources.
Commercial Models: Powered by third-party vendors (e.g. OpenAI, Microsoft). They are scalable and easy to deploy, but may have limitations in terms of customization and data privacy.

The choice depends on your business needs: whether you prioritize control and security or quick and easy deployment.

2. Communication Channels

To maximize the reach and effectiveness of your chatbot, it's important to integrate it with the right communication channels:

Web Interface: Engage visitors in real time through your website.
WhatsApp: Leverage this popular messaging platform for customer interactions.
Social Media: Platforms like Facebook Messenger and Instagram enable quick and accessible support.
Email: Automate responses and manage conversations with tools like Gmail.
Calls: Voice-enabled chatbots can handle customer service calls or provide information by voice.

Choosing the right channels depends on where your customers engage the most and how you want to interact with them.

3. The Main Prompt (or Prompts)

The main prompt is the set of instructions that defines the behavior of the chatbot. It establishes the tone, context, and expertise of the bot. There are two main options:

A single main prompt for the general behavior of the chatbot.
Multiple prompts to handle different contexts or specific types of conversations.

Designing your prompts is critical to ensuring your chatbot provides useful and consistent responses, and can be refined over time based on user interactions.

4. Memory Retrieval

To provide personalized and context-aware responses, the chatbot must retrieve memory from past interactions or a knowledge base. There are two common approaches:

Vector Data Base (RAG): Using a Retrieval-Augmented Generation (RAG) approach, the chatbot retrieves relevant information using vector embeddings, making it highly effective for large data sets and past interactions.
Via Standard Database: A simpler setup that stores and retrieves structured information, such as customer profiles or transaction history.

5. Performing Tasks and Integrating with Business Systems

Chatbots can perform actions beyond text generation through function calls. This allows them to:

Perform Actions: Call external APIs to make reservations, process payments, or send documents.
Real-time data fetching: Pulling information from APIs such as weather updates, stock prices, or CRM data.

This adds a layer of dynamic interaction, making the chatbot more functional for your business operations.

6. Chain of thought

This is an advanced feature that you can implement.

Chain of thought allows the chatbot to address more complex questions by breaking them down into a logical sequence. Instead of providing a single answer, the chatbot guides users through a step-by-step process.

This is especially useful for:

Technical support: Diagnose issues with a structured solution.
Decision making: Guide users through complex decisions such as financial planning or business strategy.

By implementing chain of thought, you can improve the chatbot's ability to handle complex and nuanced conversations.

Conclusion

Building a custom AI chatbot with these components can transform the way your business interacts with customers and automates internal processes.

If you are ready to learn how a custom AI chatbot can revolutionize your business, contact us for a free consultation on the topic.