How do you design engaging conversational agents?

In this day and age of conversational AI it’s extremely important to design engaging applications that enable users to interact with agents and get positive experience, efforts are being made to build humanoid robots that imitate human gestures, and even understand our feelings and empathize with us , we have already seen NLP TV presenters that present the news , and interview people , these developments have been made easy due to the widespread large language models, again design good looking AI agents is just as important as the message they deliver,or tasks they perform because ultimately it’s the user experience that will determine whether the agents are successful or not , the next step will be to develop their emotional intelligence

Along with traditional methods, it can be useful to leverage the data collected by platforms such as Weights and Biases, ML Flow, and Arize Phoenix. By analyzing data from these tools, your Data Science team can gain a clearer picture of user expectations. As an example, you can spot trends in user interactions that might drive tone changes in your chatbot, or clarify certain functionality!

Like any other product, you must know your users and their needs. You cannot create a conversational agent without understanding who is the end user, what they are looking for and, especially, how the conversational agent will support them. Stop creating stuff just because someone asked you to do that. Evaluate your data!

Don't forget that understanding your user base is not a one-off activity. As user preferences and technologies evolve, periodic reassessment ensures your agent remains aligned with user needs and expectations.

Understanding your users is the foundation of creating a conversational agent that truly resonates and engages. Tailoring the agent's personality and responses based on user insights ensures a more personal and relevant interaction.

Google is an AI pioneer they have designed many AI applications with different modalities such as google translate which incredibly advanced it used text to text , text to audio , audio to text and even extract text from images and translate it , this application is based on NLP it uses TTS APIs , another example is chatgpt which is an application based on Large language model with trillions of tokens it has a user interface where you put the text or prompt and receive the generated text from the AI model

An example I've seen is the rise of voice-based assistants in smart homes. While text-based chatbots dominate online customer service, voice assistants like Alexa or Google Home are preferred in home settings due to their hands-free convenience, proving that the chosen modality should align with the context and user needs.

The communication modality — text, voice, or multimodal — is pivotal in determining user engagement. Each modality carries its unique merits and challenges concerning speed, accessibility, and technical requisites like speech recognition or natural language understanding. For instance, text-based interfaces are prevalent in online customer service, while voice-based agents find favor in home settings, underscoring the importance of aligning modality with user context and preferences.

In exploring AI conversational agents, modality choice is key! Text offers precision and control, voice adds a human touch & and accessibility, while multimodal combines strengths. Consider user needs, context, and technical capabilities for optimal interaction.

Choosing the right modality for conversational agents depends on goals, audience, and context. ???Voice suits hands-free tasks, ??text is versatile, ??visual works for shopping, tutorials, ??audio is good for podcasts. Consider audience preferences and accessibility needs. Analyze user data and A/B test. Combine modalities for a richer experience. Stay updated on emerging modalities like AR/VR and haptic feedback. ??AI agents can adapt to different modalities for a personalized user experience!

Creating an engaging chatbot flow is like chatting with a smart robot. Imagine you ask, "Who won the last Cricket World Cup?" With tools like GPT/Bard/llama, the bot understands your cricket query. You can also use RASA NLU to helps it reply correctly, "England won the last Cricket World Cup." Even if you ask something unusual like, "Tell me a joke," the bot can say, "I'm here for cricket, but here's a joke to lighten the mood." This chat, backed by testing, makes the chatbot a pro. Focus should be on user needs, employ intent recognition tools, manage context, use slot filling for structured data, plan fallbacks and error handling, allow multi-turn conversations, gather user feedback, test rigorously, and ensure scalability.

Instead of hard coding all of the detailed aspects of conversation flows which can lead to a clunky, limited artificial experience - consider using a graph based approach. You can create an ontology that maps conversation outcomes to topics and data points alongside a large language model. This can you give you the best of both worlds - the power of natural language from LLMs alongside the control of the conversation topics and objectives of the dialog. This also can help reduce the amount of maintenance needed, and speed up adding new conversations to the system.

Whenever possible, leverage data from previous encounters when designing the dialogue flow. Previous chats, email volleys, phone recordings, ticket history, or other transcribed evidence of real-world interaction offer insights into how people actually dialogue with our agents (whether human or machine) rather than how we might wish users to behave.

This is the most important topic that companies usually don't spend enough time. Designing conversational flows requires time, research and a lot of testing, even before building them on Conversational AI platforms. A good design experience will improve the customer experience, engagement and trust. If you want to build a useful conversational agent, be sure you invest in this part.

From my experience, this is does not get the attention it deserves. There seems to be a lot of focus on the tech stack and automation targets, without paying attention to conversation design. At this step is where all the business processes, rules and logics are considered and built into/designed into the dialogue flow.

A human-like AI should be capable of maintaining and leveraging context throughout a conversation. This involves not only understanding and responding appropriately to prior user inputs but also demonstrating an awareness of the user's individual preferences, the history of the conversation, and the ability to bring up relevant information from previous turns to create a seamless and personalized dialogue experience. This contextual awareness should encompass not only the current conversation but also the user's broader context, including their interests, goals, and needs, in order to engage in a more natural and human-like manner.

Human-like conversational agent is the ultimate goal to build engaged experiences like humans do. Personally, I think we need to clarify to users that they are talking to a bot. This is good for managing some expectations and also will help the users how to communicate with the conversational agent.

I might suggest this is what you're ultimately optimizing for when it comes to design and continuous improvement. The more human-like the experience, the faster / easier etc it is for a person to get what they need / want sorted out in a way that feels natural and intuitive. Leaving only the "irreducible friction" aka a phenomenal experience.

Designing human-like conversational AI demands a blend of technology and psychology. Initially, a solid understanding of natural language processing (NLP) and machine learning is crucial. Equally important is embedding empathy, contextual understanding, and conversational memory in the AI. Engage in iterative testing with real users to gather feedback and make necessary refinements. Incorporating a sense of humor or culturally relevant references can also make interactions feel more natural. Lastly, continuous learning from human interactions and staying updated with evolving AI technologies will progressively make the conversational agent more human-like and adept in handling nuanced conversations.

Human-like interactions bolster user engagement, trust, and satisfaction. Techniques like personalization, emotion recognition, and sentiment analysis are employed to make the agent responsive and relatable. However, it's paramount to strike a balance, avoiding overly human-like demeanors that might spawn unrealistic expectations or ethical quandaries.

Optimization is a must-have. Check your KPIs, and get real user feedback: launch, review, improve, repeat. If you don't spend time tuning your bot it will soon miserable start failing. Treat your conversational agent as a product, not a project.

There's a commom misconception that you can develop a conversational AI and it will just work. Successful conversational AIs need constant adjustments and monitoring. If you have good monitoring, it would be easier to find areas that need improvement. Having KPIs in place woukd also help to prove that conversational AI is delivering value.

Choose and select your evaluation metrics. Appropriate metrics to measure the performance of your conversational AI. Common metrics include: Task Success Rate: The percentage of user queries or tasks successfully completed; Response Time: The time it takes for the AI to respond to a user's query; User Satisfaction: Gather user feedback through surveys or ratings; Accuracy: Measure the system's accuracy in understanding and responding to user queries; Engagement: Assess the system's ability to keep users engaged in the conversation.

A cyclical process of evaluation and improvement, grounded on user feedback, data analysis, and performance metrics, is vital for the evolution of the conversational agent. Monitoring, regular updates, and a keen eye on KPIs ensure the agent continues to meet user goals and expectations while adapting to emerging user needs and technological advancements.

??Evaluation of conversational agents involves: 1.??Metrics:Measure accuracy,fluency & coherence 2.??Knowledge:Verify information and fact-check 3.??User feedback:Collect and analyze user ratings 4.??AI safety:Ensure ethical, unbiased& secure interactions ???To improve, focus on: 1.??Naturalness: Enhance responses to sound human-like 2.??Diversity: Vary responses to avoid repetition 3.??Knowledge base: Keep information up to date 4.??Engagement: Make conversations interesting and dynamic 5.??AI ethics: Eliminate biases and controversial content 6.??User customization: Allow personalization options 7.??Testing: Continuously iterate based on user feedback ??Continuous evaluation and refinement will lead to more engaging conversational agents

Ethics is a really important factor in design. As we design AI applications to become more human-like, we need to be cognizant that we don’t allow AI to perpetuate the existing biases. It is important that certain ethical guidelines and guard rails are set for the conversational agents from day 1 of starting the design.

In my experience, consistent user feedback and iterative design are crucial. While guidelines provide a foundation, each user base has unique needs and preferences, and continuous refinement based on real-world interaction often leads to the most engaging conversational agents.

While it is important to make it human-like, at no point it should deceive the user into thinking that they are chatting with a real person. EU and many other laws specifically call out this and it would be illegal. Robust testing should be done to be compliant

Adhering to general best practices and guidelines for chatbot design is a good starting point, but it's imperative to also account for industry-specific regulations and nuances. For example, a best practice in one sector could actually represent an ethical or legal issue in a highly regulated industry like pharmaceuticals or healthcare. This principle extends to other tightly regulated industries, like insurance and banking, where failing to adhere to industry-specific guidelines could easily result in legal consequences. Customising our chatbot strategy according to the unique needs and regulatory frameworks of our industry is not just a matter of compliance, but also a key to maximising effectiveness and user satisfaction.

Adhering to established guidelines and best practices fortifies the design process, ensuring quality, usability, and ethical soundness. Continuous learning from academic research, industry standards, and platform-specific documentation, coupled with staying abreast of the latest trends in AI and human-computer interaction, informs and inspires design decisions.

Data privacy and integrity must be taken into account when building conversational agents. When building agents, it is important to consider data privacy as a key aspect. Enforcing and deploying policies and rules is necessary to comply with data compliance.

In our goal to develop conversational agents, it's paramount to remember that beyond the technological prowess, our primary aim is to emulate the depth of human dialogues. While various modalities offer unique ways to engage, the essence of a successful agent lies in its ability to resonate with users, tapping into their emotions, expectations, and cultural nuances. As we push for "human-like" agents, we must balance technological advancement with genuine empathy, ensuring our creations serve not merely as tools but as trusted companions in our digital world.

Data Privacy and Integrity: Ensuring robust data privacy and compliance frameworks is fundamental when designing conversational agents. Ethics and Bias: Ethical guidelines should be established from the outset to prevent perpetuation of biases and ensure ethical interactions. User Feedback and Iterative Design: Continuous refinement based on real-world interaction and user feedback often leads to the most engaging conversational agents.

Being context aware and setting the agent’s personality and tone of the speech to align user’s expectations whether it’s friendly, professional, or casual is important. Clear ‘Calls To Action’ to guide users effectively is another important call-out I want to make.

The key to designing engaging conversational agents lies in personalization and effective error-handling. Let's start with personalization—something as simple as the agent using your name can make a world of difference in how the conversation feels. The agent should also be equipped to offer greetings based on the time of day, apologize when it can't fulfill a request, and thank you for using the service. These small nuances add a human touch and can significantly enhance user engagement. The agent should be programmed to not just identify when it can't resolve an issue but also to communicate that effectively to the user. There's nothing more frustrating than being stuck in a loop with a bot that doesn't understand your problem.