What are the most common design patterns for chatbot development?

Chatbots use mostly Observer, Factory, MVC, Interpreter and State design patterns. But it may extend based on the additional requirements.

In my experience, it depends on what problem you are trying to solve. I have developed bots with linear, branching and mixed flows. Sometime back I created a bot for patient to book appointment with hospital. It was a combination of mixed workflow. It was linear in a way where user had to follow predefined steps in order to book an appointment with the doctor and it was branching in a way where every step had few options. So instead of user typing the intent, bot was giving multiple choices based on which experience was rendered. Designing such workflows sometimes takes time but are accurate and less confusing for users because bot is able to present options to user to choose from.

In NLP: In NLP, common patterns include intent recognition, entity extraction, and context management. Intent recognition identifies the user's purpose, entity extraction extracts relevant information, and context management maintains the flow of the conversation for a more coherent interaction. Natural language processing (NLP) understands user input through various techniques like tokenization, part-of-speech tagging, named entity recognition, and syntactic analysis. These processes help break down and analyze the structure and meaning of sentences, allowing the system to interpret and respond to user queries effectively.

The Retrieval Augmented Generation (RAG) pattern is a hybrid approach in natural language processing that combines the strengths of retrieval-based models and generative models. Retrieval models are employed to find relevant information from a knowledge base, while generative models take over to produce contextually rich and human-like responses based on the retrieved content. This pattern is widely used in conversational AI and question-answering systems, aiming to provide accurate and factual answers by leveraging the efficiency of retrieval-based methods, while also incorporating the creative and flexible nature of generative models for a more engaging user experience.

Use a modular, plug-and-play NLP architecture with standardized APIs so that rule-based, ML, and hybrid components can be mixed and matched. This enables continuous NLP upgrades without re-engineering the entire chatbot.

I have always used an external service that will determine the design pattern to follow. It may use its own model or federate already available model or services on the internet. So crux here is to design loosely coupled system which allows bot to act on user input without impacting the client code as much as possible.

The new patterns, while still in evolution, require a framework that undertakes retrieval of past interactions, both human and through chatbots, to draw out the continuity and context that currently is lacking in a lot of bot-interactions, even the ones using cutting-edge NLP. Knowledge graph adoption and creation of accurate customer interaction journey maps is where the future lies for these patterns to build the next level of chatbots.

They are all correct choices. One thing that I have found helpful is that how well we understand the end users who will use our service and what all different endpoints they will use.

State management can be very handy in building robust experience but it also involves use of system resources as each hit results into calls to multiple resources. Is the system highly transactional or an anonymous one? Should system react on basis of previous messages? etc..

Consideration for AI hallucination is important to build trust. Build in path to know when the chatbot doesn’t know the answer with certain percentage of confidence or not understand the question. Bring it forward like a good human listener would. Ask for clarification and then give answers with qualifications or simply ask for additional help from a human.