The world we live in is a world of machines with an extraordinary ability to learn and make decisions almost as we humans do. This phenomenon known as Artificial Intelligence is not just a product of science fiction or futuristic imagination. Quite the opposite, it has become an awe-inspiring reality shaping and transforming our day-to-day lives and the environment of numerous industries.
As AI is changing all aspects of today’s world, the world of app development is not an exception. AI can transform your customer’s device into a personal assistant or a language guru, and that is just the tip of the AI iceberg. The integration of AI in app development unlocks a huge range of possibilities for you and your business idea.
Let’s discover its depth and see how it can be helpful for you too! 🗻
Let us first understand AI in apps.
Simply put, AI can be viewed as the brain making apps smarter and more interactive. Those brains are amazing because they have some cool superpowers. The first one is Machine Learning, which is a super adaptable brain that can learn from data and past experiences. Algorithms of ML deal with massive amounts of information to improve task completion. Because of ML, machines can recognize patterns, make predictions, and even understand your preferences.
Another component making AI a smart ass is Natural Language Processing which works as an AI language translator that allows you can talk to your app while the app somehow understands you and can respond back. NLP makes the conversation between you and your app more human-like.
AI-powered (mobile) apps enhance user experience mainly by providing personalized recommendations based on analysed user behaviour. Moreover, it learns and grows with customers. The more your customers use your apps, the smarter they become – tailoring their suggestions to your customer’s ever-changing wants, predicting their move, and serving up their needs.
AI algorithms can provide a better user experience through user data analysis. The benefits of this personalization are significant for user engagement and satisfaction. It enhances user experience, improves discovery, saves time and effort, increases user retention, and benefits businesses through improved conversion rates and customer loyalty.
They integrated AI into education to deliver highly personalized language lessons, affordable and accessible English proficiency testing, and more. Their mission is to make high-quality education available to everyone worldwide, and it is made possible by advanced AI technology. They took advantage of GPT-4 to make learning with Duolingo even more powerful –introducing to us Duolingo Max!
Meditation, wellness, and sleep app Calm is using Amplitude - a machine learning-driven marketing database, to increase engagement and customer retention, which claims to offer a digital optimization service that can enable apps to perform better through AI.
Cleo is a chatbot that uses artificial intelligence to make managing your finances fun. The chatbot or digital assistant links with your bank account to track your spending, manage your budget, and recommend how much you can save. It will even play games and get involved in friendly and sometimes not-so-friendly banter with you. You can simply get chatting with Cleo by downloading the app, and the more it gets to know your spending patterns, the better Cleo’s algorithm and recommendation will be.
Chatbots are becoming increasingly popular for improving user experiences and enhancing efficiency in various applications. But imagine that chatbots could answer only the questions about the content you would provide them. This was exactly the challenge we faced when working with our client. The main idea was for them to provide some internal documents, and our job was to build a chatbot that would only answer the questions regarding that document. This is how we get started when building a chatbot.
When developing a chatbot we first need to define the purpose of the chatbot. Do we want it to answer frequently asked questions, provide specific information from documents, or assist users based on the content of the documents?
After we have defined our objectives, we need to select a suitable framework. Some of the more popular frameworks are OpenAi’s ChatGPT or Google’s Dialogflow.
After that, we need to gather the documents we want our chatbot to work with. These could be manuals, FAQs, textbooks, or any other text-based documents. We need to ensure they are well-structured and organised, as this will make it easier for the chatbot to extract relevant information.
Depending on our selected framework, we will need to train our chatbot to understand and respond to user queries. After training our chatbot we need to implement document retrieval which is the most crucial step. First, we need to find a good way to store our documents and also a good way of retrieving them. We can use the database's search capabilities or integrate an external search engine like Elasticsearch to efficiently search through your document database. Elasticsearch, for example, is designed for full-text search and can provide fast and relevant results.
After we have managed to do all of the following, it's time to test our chatbot. We need to collect user feedback and use it to improve the chatbot's responses and accuracy.
Some of the main challenges when developing chatbots are:
- Scalability: As our document repository grows, managing and maintaining the chatbot's performance can become more complex.
- Privacy and Security: If our documents contain sensitive information, we must ensure the chatbot handles data securely and respects privacy regulations.
- Continuous Learning: Chatbots should be able to adapt and learn from new documents or updates to existing ones.
Our client had an idea of generating some recommendations, tips and analysis based on the user's input. But this can be generalised to many different use cases. So how do we get started with generating the content based on the user's input and client's ideas?
First, we need to clearly understand the use case. What kind of interactions or responses do we want to generate based on user input? Next, we need to figure out how to process the user's input and combine it with the prompt. Here prompt engineering comes into play. But why do we even need prompt engineering? The goal of prompt engineering is to guide the model to produce desired outputs or responses that align with a specific task or objective. It involves a lot of trial and error. We need to create the best prompt for our use case in order for the best responses to be generated. We need to find a way to format the user's input in order to join it with the prompt and for the model to understand it. In the prompt, we also need to specify the format of the output we want to receive. This was our main challenge here. Because the client wanted the response to consist of many different data structures, we had to be precise in defining the format of the output in order to be able to parse the data. This was a great challenge because language models can be non-deterministic, which means we can't 100% predict the output they will provide. But with good prompting, we managed to make it work. We used OpenAi’s text completion API.
AI integration is a strategic choice to deliver exceptional user experience and take the success of your app projects to another level. This is the present and the future of app development. Add to it also the low-code development approach and you have your winning combination which is a fast and effective process of your idea coming to life.