The Advancement of AI Chatbots for Business

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The age of AI is here and it has the power to transform various aspects of our lives. From customer support to code generation, AI has become pervasive in today’s world. However, AI tools haven’t always been as advanced as they are now.

The first AI tools had significant limitations, lacking the ability to understand context or learn and improve on their own.  For example, the first chatbots were rule-based, meaning they were developed based on predefined rules or scripts. This severely restricted their capacity to only perform tasks that were programmed into them.

AI Chatbots have now become very advanced and can be used for Webchats, Lead generation, Social media marketing, Whatsapp and SMS.  

Why You Should use an Advanced AI Chatbot Builder?

  • Custom chatbots can be complex to build and maintain. No code Chatbots are easier to build using an intuitive interface.
  • Bad chatbots can negatively effect business
  • Integrating chatbots with existing systems can be a complex process. They make it easier to integrate using API’s and webhooks.

Applications of AI Chatbots in Various Industries

AI chatbots are integral in numerous sectors, aiding companies in streamlining operations, boosting productivity, enhancing user experiences, and refining customer service. Here are some examples of how AI chatbots can be utilized in your organization:

Customer Support:

  • Offer round-the-clock assistance to clients.
  • Respond to frequently asked questions.
  • Resolve simple issues via chat.

Sales and Marketing:

  • Handle initial product or service queries.
  • Suggest recommendations.
  • Assist in making purchasing decisions.

Content Creation:

  • Generate ideas for content.
  • Create outlines for articles.
  • Draft emails, social media content, and various written segments.

Recruitment and Human Resources:

  • Interact with prospective job applicants.
  • Provide answers to basic queries about job openings.
  • Arrange interviews and meetings

An Overview of the different types of Chatbots

Pure AI Chatbots

Use ChatGPT, free form conversation, run on and endless loop. Run on LLM API’s (eg. ChatGPT API)

LLM’s definition: A large language model (LLM) is a type of artificial intelligence (AI) algorithm that uses deep learning techniques and massively large data sets to understand, summarize, generate and predict new content

There are two types of Pure AI Chatbots:

1.Prompted assistants

LLM API + Prompt. You can set it into mode or character.

Uses ‘baked in’knowledge of LLM

Helpful but not valuable

This works by combining a large language model and a prompt.  Simply give it some  instructions and it will respond in a certain way.

This type of Chatbot can be helpful but they’re never really going to be hugely intelligent because you’re just giving it an instruction and it’s baked in responses.  Creating a more intuitive Chatbot for a business needs to look at more layers of information like using a knowledgebase.

Custom knowledge Chatbots

Next we have custom knowledge Chatbots – the same as a prompted assistant but you have the added element of a knowledge base or maybe a business’s data warehouse.

We could use a spreadsheet as a source or even a customer service document and create a custom knowledge Chatbot that has access to business data.

There are so many different applications and use cases. 

Basic AI Chatbot Examples:

  1. Basic customer support Chatbot
  2. Basic AI Persona Chatbot
  3. Basic Lead Generation Chatbot
  4. Basic Staff Training Chatbot

Modern /Advanced AI Chatbots

These are more advanced and are structured Chatbots but with AI elements throughout and these can perform actions (pull/push data to a database, API’s) to pull targeted information into the conversation.  Extremely valuable to a business with a nunber of use cases.

Advanced AI Chatbot Examples:

  1. Advanced customer support Chatbot
  2. Advanced AI Persona Chatbot
  3. Advanced Lead Generation CHatbot
  4. Advanced Staff Training Chatbot

AI Software Types

Prototyping Chatbot Software

Used to create a proof of concept (POF) Chatbot and provide a demo using custom data. Limited customisation.

  1. Chatbase
  2. Dante
  3. Cody AI

Complete Bot Builders

Build more advanced bots, that can perform actions and API calls, read and write to Database outside the Chatbot.  Some examples of apps you can deploy to are Whatsapp and SMS.

  1. Botpress
  2. Voiceflow

Chatbot Software Tools

Provide automation to integrate the Chatbot with other systems and trigger events using say Zapier.

  1. Zapier
  2. Stack AI

Building an AI Chatbot: Key concepts


A database of text and numeric data chunks stored by similarity. A chatbot can retrieve a small amount of information most similar to the a query from the database.  This information is passed to a model such as chat CPT API to help  generate an answer to the question.

Appearance versue reality:  with a correctly configured knowledgebase, you can create a chatbot that appears to ‘know’ all of the information. In reality it doesn’t. 

Fine Tuning

It’s impossible for them to look at all the data at once due to Token limits of ChatGPT. Model API’s have a fixed limit on information we can provide them. What we have is the capability to get the most relevant information to an answer query.

For this we use ‘Chunking’. So for example a large encyclopedia would be broken down into discrete parts containg a some of the data/information.  This would then be stored in a vector database.


This takes the form of a written  set of instructions to AI to illicit a desired response. You can tell the AI to act as a specific persona, for example a ‘marketing expert’  and ask it write copy for a Facebook advert. The options are endless. To get the best out of the AI, it’s best to learn all about Prompt engineering.

Intent Classification

Is defined as the task of taking a written or spoken input and classifying it based on what the user wants to achieve. So for example, asking for an Order Number, a review or reply using a knowledgebase answer as default.

Chat History

So in this scenario we incorporate recent chat history as context, alongside the user query. Result = Prompted AI + History.


How is the Chatbot going to be deployed.  Webchat, Whatsapp, Messenger or SMS.

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