What is a chatbot?
Generally speaking a bot is any software that performs an automated task, however, we are interested in the class of bots that live online in chat platforms or on social media called chatbots.
In this context, there are many possible definitions and some confusion about what a bot is. This is partly because there are so many varied use cases for bots and these influence what people perceive a chatbot to be.
The most intuitive definition is that a bot is a software that can have a conversation with a human. For example, a user could ask the bot a question or give it an instruction and the bot could respond or perform an action as appropriate.
Relevance of Chatbots in business
In our present era, customers have the ability to make a good judgment. They all are very much active on social media platforms in order to be updated regarding new technologies or news.
As customers are more practical towards technologies, therefore, they demand near real-time response to their queries and expect more personalized recommendations/ suggestions from the brand. In order to satisfy such kind of customer demands, brands/ businesses are turning their focus to build chatbots or virtual agents that can manage customer queries round the clock without any delay.
To be very precise, chatbots engage their customers in the right place, at the right time, with the right information in a cost-efficient way.
Basically, chatbots behave like human beings in such a way so that customers can build trust and empathy towards the particular brand/business.
Advantages of Chatbots
They can be available 24/7 round the clock to solve the queries of the customers. As they are basically virtual robots they never get tired and continue to obey your command.
B. Save Money
Chatbots are virtual machines therefore, they can reduce the headache of managing thousands of employees. And the saved expenses over employees can be used to grow business.
C. Managing customer
As they are not humans, they can handle a number of things at the same time by conversating with thousands of people, no matter what time of the day it is or how many people are contacting you, every single one of them will be answered instantly.
All chatbots are a form of AI, and all chatbots are supported by complex programming. However, they fall into two categories. The way they are split depends on whether the dominant feature is Sequential and intelligent chatbots
They are nothing but follow a conversation flow defined by the maker. These chatbots have some kind of restraints that is they cannot go out of this scope(which maker has already defined.
2. Intelligent Chatbots
Chatbots comes under this category contain AI technology and Natural Language Processing algorithms, which are able to understand what the user trying to say and understand the intent of it.
Chatbots that function on structured questions and answers are less complex and chatbots that function on machine learning harness the full power of AI. From the front end they both look similar and as a user, you may not be able to distinguish the difference. A chatbot working on hardcoded questions/answers has a smaller knowledge base and skill set, and can only provide the correct output to specific instructions.
Why choose Rasa?
There are plenty of easy-to-use bot building frameworks developed by big companies. if you are building conversational interfaces in real-world solutions, you are likely to start with relatively simple dialogs that can be modeled as a one-page flowchart. For that type of scenarios, its incredibly tempting to go with the NLP stacks from cloud AI incumbents such as Microsoft Cognitive Services, AWS Lex, Watson Assistant or Google’s DialogFlow. After all, those solutions seem to encompass everything you are ever going to need on a conversational solution.
However, very quickly NLP scenarios start experiencing challenges that seem to fall outside the domain.
Challenges with this type of NLP cloud services
1) Dialogs need complex integration with external systems.
2) The conversations grow and start producing a large number of calls to the cloud NLP services making the cost unmanageable.
3) Developers are constantly changing the dialogs to accommodate new interactions.
4) The conversations grow to a level that can’t be modeled as a simple flow chart.
These challenges are very common in large-scale NLP solutions. Think about it, part of the magic of human conversations is that there are infinite ways to arrive at the same point or express the same intention. RASA is an NLP platform that tries to address these challenges.
This is where Rasa platform comes incredibly handy. It is an open source, Python-based NLP stack that enables the implementation of highly sophisticated conversational interfaces. It doesn’t have any pre-built models on a server that you can call using an API, which means that it will take more work to get it running.
In principle, RASA moves away from the traditional flowchart model of natural language dialogs by introducing probabilistic models that control the flow in a conversation. The RASA platform is based on two fundamental components:
Rasa NLU is responsible for natural language understanding of the chatbot. Its main purpose is, given an input sentence, predict an intent of that sentence and extract useful entities from it.
Rasa Core is the next component in the Rasa stack pipeline. It takes structured input in the form of intents and entities (output of Rasa NLU or any other intent classification tool) and chooses which action the bot should take using a probabilistic model (to be more specific, it uses LSTM neural network implemented in Keras).
The cool thing about Rasa is that every part of the stack is fully customizable and easily interchangeable. It is possible to use Rasa Core or Rasa NLU separately. When using Rasa NLU, you can choose among several backend NLP libraries.
Get in touch with us to implement chatbots for your business.
If you’re planning to go into more detail about Rasa stack, the best way to learn what it is capable of is to work through the tutorials available at Rasa Core and Rasa NLU documentation websites. Here are a few readings and tutorials to go deeper.
Build a small bot of your own: https://rasa.com/docs/core/quickstart/