In this post I like to introduce a powerful Chatbot Content Management System (Chatbot CMS) for managing chatbot’s content dynamically. To demo what is possible, I have created a COVID-19 chatbot using Dialogflow, Google’s conversational chatbot platform and Botcopy a rich custom web-chat. A Chatbot CMS platform will allow us to create, manage and access our bots content outside of the chatbots dialog-flow. This means instead of hard-coding our bots responses into flows and components, we can have our flows and components access our Chatbots CMS to find specific answers. Moreover, this enables us to change, edit and re-organize our content easily and hence improve content management for our bot. In our example for COVID19, we are able to add new content daily, re-categorize our content, predicated on user interactions and new emerging content. Structured Content: Key to successful chatbots and AI For chatbots to work well with outside content-sources we need structured content. There are three main elements to chatbot interaction. Context – the reason for the interaction Intent – the purpose or goal of the interaction Entity – keyword property used by Dialogflow to retrieve information Using our KBucket content library we can achieve all of these objectives elegantly in building our AI chatbot. Effective Context Management in Chatbots Context, in terms of our chatbot, is the reason for our conversation. Effective context management in AI chatbots is important because it allows bots to interact with users in a way that is easier, quicker more helpful and less robotic and scripted. In our example we are using COVID19 as our context, and have built an AI chatbot to answer questions related to the COVID19 pandemic. Using our Chatbot CMS platform we are able to curate articles and documents related to our topic (context), and structure it for access from our bot, using a custom webhook. The result is a COVID-19 content library with over 250 articles to date, answering critical questions about the pandemic, its risks, suggested precautions, infection data, mortality charts, guidance, immunity, testing, vaccines, lockdown information, reopening related guidelines and more. By organizing all the information related to our project in one place we are able to answer any related question in a natural conversational manner. Our COVID19 content library is now the source of information for our chatbot and any update to the library is automatically included in our bots response. As an organization, using the KBucket system, we can easily organize information from different sources, add expert summary to each item and organize it for access via one or more chatbot agents. The source for a post in the KBucket library can be a web-page, links to web documents, social media posts and basically any asset that can be accessed in our browser. Each saved link has the following components, which is structured and consistent: Title: Your posts title URL: Link to the post Image URL: a custom image for that post Description: A summary of the post Publisher: the publisher of the post Date: The date the publication was published Revision Date: The date the post was updated by the curator Author: The author of the post Content tags: Categorizing the content in the post Creating and Optimizing Intents for Chatbots Intents in Dialogflow, and for chatbots in general are questions related to our topic. In other words, what does the user want to know about the information we are sharing and how will they be asking related questions to find their answers. By researching and curating content related to our topic we are in essence building an Intent Map. Every title and description for each post, is a guide for creating a new “Intent”, and a guide for authoring the “Training Phrases”. The content in each post informs us of the best training phrases for our intent. For example the articles we have curated related to how “children” are affected by the coronavirus has informed our training-phrases as follows: A Powerful Knowledge Transfer Tool for Building Conversational Chatbot The KBucket CMS solution is ideal for agencies, or any outside group building conversational chatbot for clients, because it makes the transfer of knowledge much easier. Let your clients curate, organize, comment and categorize their content the way they work and talk, and then design their chatbots using their own language. This will save both you and your client valuable time and deliver a better product. Your clients can be both internal to your company or external. The key is to learn the language of the people who will be using the product, to author your Intents and training-phrases. Entities: Delivering the right content for your AI chatbot An entity is a property which can be used by Dialogflow to answer a request from the user. The entity will be a keyword within the request such as name, date, location, publisher, author, etc. Our structured data lets us use content-tags, author-tags, publisher-tags, and date-tags as entities to retrieve specific posts based on the users inquiry. In our COVID19 chatbot example we have made use of one or a combination of content tags to build our intents. Let’s take a look and see how it all works. Content Category Tags as Entities In our COVID-19 demo bot, we have divided our entities into two groups. Single tags: Here we provide more general answers to questions like “give me the latest news on vaccines“. Multiple tags: Here we use a combination of tags to answer more specific questions like what are the COVID19 guidelines in california. Date Tags as Entities Dates are used as system entities in Dialogflow. Since each post in our KBucket library has a date stamp we can use these entities to build special responses from the same intent. By simply adding new training phrases that include dates for each intent we can now ask more specific questions that filter responses according to their published dates. We have provided examples in our bot (it’s at the bottom right side of this page), and encourage you to try using similar queries for other intents. Conclusion We have made powerful arguments for using our CMS solution to build and manage your chatbots. We learned that using curation and the KBucket content library as our Chatbot’s CMS platform allows us to organize and segment our content for use in our AI chatbot. We have also discussed how the content stored with each post can help us quickly build “learning phrases” for each intent in our conversational chatbot. We also learned that using our KBucket chatbot CMS solution lets us effectively manage the context of our conversations and deliver less robotic and scripted conversations. With this understanding, we are now able to build complex chatbot conversations with multiple agents, each responsible for a certain project or area of interest, accessing a different KBucket channel for information! We offer training for using our curation system for use with Dialogflow. Anyone who signs up for the training will also receive the webhook code and the training required to customize it for their own use. Contact us if you like to learn more. You can schedule an appointment here.  

The post A Powerful Chatbot CMS Solution for Conversational Chatbots – AI chatbot appeared first on Optimal Access.