How to build an AI app: a step-by-step guide

13 min read
October 2, 2023

Building an AI app is the next big thing in the app development world.

And for a good reason.

AI can help you take your app idea to the next level and give your users a much better experience.

So, if you’re looking to build an AI app but don’t know where to start, we’ve got you covered.

In this article, we’ll give you a detailed AI app development guide..

Let’s go!

Set the goals for your AI app

If you want your AI app’s development to be successful, the first thing you need to do is set the goals you want to achieve with your app.

Like motivational speaker Tony Robbins, said: “Setting goals is the first step in turning the invisible into the visible.”

Having clearly defined goals will ensure your development process stays purposeful and targeted.

They’ll guide your app and help align stakeholders with your vision.

And that’s why every successful AI app had clear goals that guided its development.

The best place to start is by defining the problem your app solves for your users.


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And all of your goals should be focused on solving those problems.

Take Grammarly, for example.

The goal of their app is clear – provide real-time grammar and style corrections to improve their users’ writing.

And that’s exactly what it does.

After you’ve defined your main goal, you need to set the key performance indicators (KPIs) you’ll use to track your AI app’s success.

Your KPIs should follow the SMART framework, as shown below:


source: Mad Devs

Some AI-specific KPIs you should set are:

  • Model accuracy
  • Model precision
  • Model recall
  • F1-score
  • Model fairness

These KPIs will help you track and assess the performance of your AI model.

Another important thing you should do is set clear development and deployment milestones.

This shouldn’t be a problem if your team is using an agile development methodology like Scrum.

Agile methodology

An Agile approach to development will help you build your app faster.

Here’s what DECODE co-founder and CEO, Marko Strizic, had to say about how Scrum (and Agile more broadly) should work:

Also, make sure you evaluate the resources you have at your disposal before you start building your AI app.

One major drawback of AI models is that training and maintenance can take up a lot of resources.

You need to ensure that your infrastructure can handle the increased load.

In short, the goals you set at the start of your AI app development will guide you throughout the development process.

That’s why you need to get them right from the start.

Choose the right tools and frameworks

Your AI app’s success depends on the right choice of tools and frameworks to build and train your AI models.

The framework you choose will depend on your specific business needs and your existing infrastructure.

The right tools and frameworks will help you cut down on development time and get the most out of your AI model.

Let’s start by discussing some of the most popular AI frameworks like:

TensorFlow is an open-source library for building and deploying machine learning models.

It’s suited for more complex projects and for commercial use.

TensorFlow excels at image recognition models in particular.

For example, Airbnb used TensorFlow to create an AI image classification model, in order to better categorize listing photos.

Also, TensorFlow has an end-to-end platform, TensorFlow Extended (TFX) – with TFX, you can manage your AI model from training to deployment.

PyTorch is another popular open-source AI library.

It’s a good choice if you’re looking for flexibility and a modular design. 

But, PyTorch has one major drawback.

It’s geared towards academic researchers and doesn’t offer commercial support.

Finally, Google AutoML is a good choice if your team doesn’t have experience with AI models.

With it, you can train a custom model in minutes with minimal effort and expertise.

It’s a good choice if you’re planning to build a simple AI model.

But, these AI frameworks aren’t your only options.

You can also go with cloud-based AI platforms, such as:

All of these are end-to-end platforms on which you can build, train, and deploy AI models into your app.

And the best part?

One of these platforms is likely your cloud computing provider, as they’re market leaders in that segment.

This will make integrating and deploying your AI model much easier.

In short, you need the right tool for the right job – your AI app’s success depends on it.

Collect and prepare data

Data collection and preparation is a key step during AI app development.

Without high-quality data, even the best designed AI model will perform poorly.

You should always prioritize data quality over quantity.

Otherwise, it could cost you.

And we mean that literally – a Gartner survey showed that organizations believe bad data quality costs them an average of $15 million per year in losses.

To avoid losses like that, you need to make sure your data is properly cleaned and formatted.

But, first you need to choose the datasets you’ll use to train your AI model.

Luckily, there are a number of publicly available datasets you can use.

Let’s say you’re training a natural language processing model.

In that case, Common Crawl is a good option – it’s a free, open repository of web crawl data.

OpenAI used Common Crawl for 82% of the raw tokens they used to train their GPT-3 model.

And if you need more specific datasets, Kaggle and AWS Data Exchange host open datasets you can freely use for your AI model.

Once you’ve found the datasets you want to use, you need to prepare them before use.

You need to do:

  • Data cleaning
  • Data preprocessing
  • Data wrangling

Data cleaning is self-explanatory – it removes inaccuracies and inconsistencies like duplicate entries.

Tools like OpenRefine and Tibco Clarity are good choices for that task.

Next, you need to preprocess your data – data cleaning is just the first step of data preprocessing.

Data preprocessing

source: V7 Labs

Data preprocessing also involves:

  • Data transformation
  • Data integration
  • Data normalization
  • Data smoothing
  • Data reduction

In simple terms, the point of data preprocessing is to improve the quality of the data you’re using.

Once you’ve preprocessed your data, your next step is data wrangling.

This is when you turn your raw data into a usable format for AI models.

And after you’ve done that, you can start training your AI model on that data.

To sum up, data collection and preparation is a key step when building an AI app.

And you need to do it right.

Design and train your AI model

Once you’ve prepared your data, the next step is designing and training your AI model.

Your AI app’s success depends on a properly designed and trained model.

The model is at the heart of your AI app and it needs to be up to par.

So, how do you design an AI model?

First, you need to decide on your training approach.

The 3 main approaches are:

  • Supervised learning
  • Unsupervised learning
  • Reinforcement learning

The choice of approach will depend on your specific needs.

Supervised learning, where the data fed into the model is labeled and the model has access to the correct answers, is the best approach if you need better accuracy.

For example, it’s used for image recognition and price prediction models.

Unsupervised learning, where the AI model learns on its own, is better for more dynamic models like recommendation systems or fraud detection.

And reinforcement learning, where the AI model’s desirable behaviors are rewarded, is a good choice for natural language processing models.

ChatGPT is an example of an AI model that uses reinforcement learning – that’s why it’s a good idea to point out when it makes a mistake.

Once you’ve decided on the training approach, you need to design your model’s architecture.

Some of the top architectures are:

  • Convolutional neural networks (CNN)
  • Recurrent neural networks (RNN)
  • Generative adversarial networks (GAN)

Just like with the training approaches, your choice of architecture will depend on your specific needs.

For example, you’d use CNNs for a recommendation system, RNNs for speech recognition, and GANs for image generation.

Next, you need to start training the model on your data.

Your first step should be to split your data into:

  • Training data
  • Validation data
  • Test data

A good ratio is:

  • 70% for training
  • 15% for validation
  • 15% for testing

This way, you’ll ensure optimal performance.

The next step is training the AI model.

This is when you feed the model with your data so it can recognize and learn from the patterns in the data.

After you’ve done that, you need to review your model’s accuracy and reliability.

This is where the KPIs we’ve mentioned come in.

And once you’re sure your model is performing well, it’s time to integrate it into your app.

Integrate the AI model into your app

Integrating your AI model into your app is the most critical phase of the AI app development process.

It’s the make-or-break moment for your AI app.

That’s why you need to do it right.

For starters, you need to decide where you’ll integrate your AI model – in your app’s front-end or back-end.

You’ll more than likely integrate it into your app’s frontend i.e. where your users can access it.

But, if you have a speech recognition AI model, integrating it into your app’s back-end will improve its accuracy.

Next, you have to decide how your AI model is going to process data – on the cloud or on-device.

Once again, this will depend on your specific needs.

Cloud computing vs edge computing

source: Cardinal Peak

On-device processing, or edge AI, is especially useful if you’re developing an AI-powered Internet of Things (IoT) app.

For most other purposes, cloud-based data processing is the better option.

Also, integrating an AI model into your app doesn’t need to be complicated.

You don’t have to reinvent the wheel.

Make sure to check if there are any existing AI APIs on the market, as this can save you a lot of development time.

For example, Google Cloud AI offers the Google Cloud Vision API.

This way, you can integrate AI image analysis capabilities without building an AI model from scratch.

Another thing you should do is create a feedback loop for your AI model.

AI feedback loop

source: UX Collective

This way, your users can leave feedback and rate your AI model’s performance.

And this will allow you to improve its performance.

Also, make sure you inform your users about your app’s AI-powered capabilities.

Being transparent about what your AI model does can help you build trust with your users.

Of course, you should also make sure that any interactions they have with your AI model are intuitive.

Even if your model is complex, keep user interactions simple.

Remember, AI app development isn’t just about plugging an AI model into your app.

It’s also about ensuring that it enhances your app’s user experience. 

Test and iterate

Your AI app’s journey doesn’t end after you’ve finished developing it.

You should treat every AI model as a work in progress.

That’s why continuous testing and iteration are crucial for your AI app.

AI models aren’t perfect from the start, they learn and evolve.

Also, AI models need to be periodically retrained with fresh data.

This will prevent your AI model from drifting.

AI model drift

source: Evidently AI

It’ll also keep it accurate and relevant throughout its lifecycle.

But, how exactly should you test your AI app?

There are three main types of testing that you should do:

  • Unit testing
  • Integration testing
  • User acceptance testing

Of course, there are dozens of other types of testing your QA engineers will do at some point.

But these 3 are the most important for your AI app’s success.

So, what exactly do you test in each of these?

During unit testing, you’re testing how individual components of your app work.

Let’s say you have an AI chatbot integrated into your app.

Testing to see if its greeting function triggers correctly is an example of a unit test.

You can also automate unit tests.

This way, your app’s components will be regularly tested without wasting your QA engineers’ time.

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Next, integration testing checks how the different components of your app work together.

Here, you’ll see how your AI model functions with your app’s other components.

It’s a good idea to simulate real-world scenarios during integration tests.

In simple terms, your integration tests should reflect how your users interact with your app.

Finally, you should do user acceptance testing.

This will ensure that your AI app and model meet your users’ needs and expectations.

It’s a good idea to have a diverse group of users test your app.

You’ll cover a range of user perspectives and get valuable insights into how they use your app which will allow you to improve its performance.

Also, use the feedback loop we’ve talked about to iterate on your AI app and model to better meet your users’ needs.

This way, you’ll make sure your AI app stays relevant longer.


Developing an AI app is the best way to create an innovative app that will resonate with your users.

But, you need to do it right.

And that’s why you should follow this step-by-step guide.

To recap, the steps are:

  • Set the goals for your AI app
  • Choose the right tools and frameworks
  • Collect and prepare data
  • Design and train your AI model
  • Integrate the AI model into your app
  • Test and iterate

If you want to learn more, check out how we build AI apps or read our article about the top 10 AI app examples you should know about.

Written by

Ante Baus


Ante is a true expert. Another graduate from the Faculty of Electrical Engineering and Computing, he’s been a DECODEr from the very beginning. Ante is an experienced software engineer with an admirably wide knowledge of tech. But his superpower lies in iOS development, having gained valuable experience on projects in the fintech and telco industries. Ante is a man of many hobbies, but his top three are fishing, hunting, and again, fishing. He is also the state champ in curling, and represents Croatia on the national team. Impressive, right?

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