If you are interested in AI chatbots and how they developed, you will want to read our step-by-step guide, explaining the various complexities that are typically involved.

We will delve into seven different stages of the chatbot development process, which consist of:

• Define the project purpose
• Plan the build
• Prepare your training data
• Begin training the LLM on your data
• Design the chatbot framework
• Conversational flow
• Testing

Step One: Define the project purpose

The beauty of AI chatbots is the near-endless possibilities when it comes to training them to perform a specific task.

Need a round-the-clock customer service agent?

An expert in your company procedures to help enquiries?

A way of checking your inventory and stock at any time?

A chatbot powered by AI could perform all these tasks and many more - but herein lies the danger.

Before you begin making your AI Chatbot, you need to have a very clear project scope in mind, to ensure that the chatbot is trained for the purpose you require. Some things to consider for the project scope include:

• What is the primary purpose of this chatbot?
• Who will be using this chatbot?
• What level of expertise and specialism will this chatbot need?
• What tasks will the chatbot be expected to handle?

Step Two: Plan the build

Once you have a plan for the chatbot, you will need to consider how to make the AI chatbot. For this, you will need a Large Language Model trained on your specific data and to perform your desired tasks. Your options for these LLMs include:

a) Open Source Large Language Models

5 of the top open source LLMs include:

• LLaMa
• BLOOM
• BERT
• Falcon 180B
• OPT-175B

If you are intending to use your AI chatbot for commercial purposes, you will need to explore the commercial terms for usage of these open source LLMs.

b) Proprietary Models

Proprietary LLMs are developed and owned by a specific company, rather than being publicly available. These types of LLMs, such as OpenAI GPT-4 and Anthropic’s Claude are controlled by their parent companies, which has certain advantages and disadvantages.

A key consideration for using a proprietary LLM is that the data that the model will have been trained on will remain private to the company.

c) Commission your own custom chatbot

AI developers such as NetGeist.ai can create custom chatbots and LLMs using your unique project requirements. By commissioning a custom chatbot, you will retain the uppermost control over the training data to ensure maximum transparency, whilst also having the ability to sculpt the chatbot’s functionality to your exact needs.

Choosing between the three options will depend upon your preferences for model size, speed, support and available budget.

If you are looking for a general, small-scale chatbot to answer basic questions, then you may be best suited with either a proprietary LLM or open-source alternative. However, if you need to train your chatbot on your own data and require it to perform specific functions for your organisation, you will be best suited to opt for a custom chatbot.

Step Three: Prepare your training data

The next stage to making an AI chatbot will be to prepare your training data. You will firstly need to gather data that is relevant to the purpose of your chatbot. A good place to start would be your website FAQs, transcripts from your customer support messages and any documentation you have for the products.

Collecting a high quality and quantity of relevant data will positively impact the success of your custom chatbot. Without good data, your chatbot will be unable to answer queries with accuracy and confidence.

Once you have collected your training data, you will need to remove the “noise” - this being the unrelated information that could skew your training process.

During this process, remove any duplicate data, fix any typos and standardize the formatting.

If you intend to perform a supervised learning task later in the process, you will need to label the data accordingly.

Step Four: Begin training the LLM on your data

We’ve written an in-depth guide on how to build an LLM that you can check out here. A simplified summary of the process of training an LLM involves:

• Tokenizing the data, converting it into a format that a model can understand and process
• Set the model parameters such as the learning rate and batch size
• Train the platform on specific relevant tasks
• Measure the accuracy and loss to validate the model performance

Step Five: Design the Chatbot framework

Once the LLM is ready and trained on your data, it is time to focus on the front and back end of your chatbot’s architecture.

Designing your chatbot’s frontend will focus upon the user interface, in terms of how your customers will interact and use the chatbot. It is imperative to create a chatbot that is easy and intuitive to use.

From a backend perspective, you will want to consider any integrations that your chatbot will require, such as into a CRM, app or database.

If you will require your chatbot to handle context across multiple customer interactions, you will need to consider memory.

Step Six: Conversational Flow

You want to ensure that your chatbot provides a consistent, predictable response to general enquiries. Everyone has seen the horror stories of chatbots gone rogue!

To make sure yours doesn’t end up going the same way, you will need to implement rule-based logic for the chatbot to follow.

To ensure users do not end up in an endless feedback loop, build in fallback mechanisms for enquiries that the chatbot does not understand. This could either be a request for the user to rephrase their enquiry, or to provide the option to contact a human support agent.

Step Seven: Testing

As with every software release, the testing phase is a vital stage that should not be rushed or ignored. Test each individual component for unexpected results and bugs.

Try to simulate real interactions as much as possible, as well as a few left-field queries to see how the chatbot responds.

Testing is usually an iterative process, with plenty of improvements and fixes made before it is ready for deployment.

Once you are happy with the performance of your chatbot, it is time to deploy it!

After deployment, it is important to regularly monitor and optimize the model. Track key metrics such as response times and user satisfaction to ensure that the chatbot is working as intended.

Where possible, continue to train the model on new data to keep it up-to-date and relevant, whilst also helping to mitigate any biases within the model.

These are the steps you will need to follow to build your own AI chatbot. Or, you can save your time and trust our expertise here at NetGeist. We build AI tools that tackle your textual challenges. The custom chatbots that we build for our customers have:

• User-friendly interfaces
• Provide accurate and reliable information
• Have multiple integration options
• Tailored solutions for your organization requirements

By creating tailor-made tools, we strive to adapt to the needs of different industries including healthcare providers, financial enterprises and government institutions. No project is too big – our goal is to develop customized NLP solutions that would fit the concept of your company.

From virtual assistance to information gathering or financial advice, receive insightful input that would boost the efficiency of your workflow.

Let us simplify your textual tasks with a unique solution, tailored specifically just for you... so if you are interested please do not hesitate to contact us.