Building Generative AI Applications: The Ultimate Guide

Generative AI has been all over the place recently, right? Well, fair enough, OpenAI's ChatGPT, DALL-E, and similar technologies are revolutionizing various industries and are forecasted to shape the future.

Although the gen AI market is already quite crowded, the field has lots of potential business-wise. You just need to find a problem that hasn't been solved yet and then harness the power of this artificial intelligence type to tackle it. This requires specific industry knowledge, in-depth market research, and an experienced team of developers to make it happen.

But let's take it one step at a time. If you have an idea for a gen AI-based application but aren't sure how to approach its creation, this page holds answers to many of your questions. Keep reading to find out whether generative AI software development is worth it now and learn how to create generative AI solutions using tips from Upsilon's CEO and CTO.

What Is Generative AI?

Noting a few generative AI fundamentals, it is a special subset of artificial intelligence and machine learning which emphasizes creating new content. Unlike traditional AI systems which mostly focus on processing and analyzing already existing or historical data, gen AI uses deep learning models to learn patterns from existing data and then produces original outputs like text, images, code, or even unique audio or video to name a few, mimicking human creativity. This means that gen AI leaped forward since it can not only think or identify patterns but also create.

In a nutshell, generative AI utilizes advanced machine learning to deliver results on its own. This becomes possible with the help of large language models (LLMs) and other deep learning architectures trained on big sets of data.

The best part is that such AI-based solutions are designed to learn. They use data and are capable of improving over time as they adapt to changes and figure out how to cater to the preferences of users more. This self-improvement allows them to deliver better results, making the technology more sophisticated in the course of time.

For instance, have you ever used ChatGPT? This generative AI tool that uses LLMs continues to learn from feedback and large data pools, making its outputs more unique, correct, and to the point. And this includes lots of output types like human-like text, code, or even art.

Applications of Generative AI

What is this technology capable of? And in which industries is gen AI applied? In lots of them already, we've seen many solutions that are able to do things like analyze customer preferences, give personalized recommendations, craft images using textual descriptions, engage in conversations with users, and more. People are getting more creative about tech startup ideas in the sector and how to use generative AI for new purposes. Here are a few examples in several fields and industries:

• entertainment (such as music creation tools for composing melodies);
• education (personal tutors like Elsa or language translation tools);
• marketing (text generation tools for content creation, email writing, social media posts, and so on);
• design and art (image generation tools such as Midjourney, retouch, and editing tools);
• sales (chatbots or personalized shopping suggestions);
• software development (help with writing code, creating API calls, data structuring, and other work);
• team collaboration (it can summarize discussions, make document drafts, generate reports, and so on);
• customer service (chatbots for communicating with clients, answering questions, and resolving customer support requests);
• and it's safe to say that other industries will soon adopt it too.

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Benefits of Gen AI to Note

What are the benefits of generative AI? And what makes this technology so attractive for entrepreneurs and various-sized businesses? Here are a few notable gen AI advantages.

• Saves time, reduces costs, and boosts efficiency — In essence, such tools can help automate some business processes and speed up others. For example, if gen AI helps teams spend less time manually processing customer queries or frees up their hands from some repetitive, monotonous tasks this may substantially help allocate resources more optimally.

• Possibly more revenue — You can "feed" information about your services or what you're selling to pre-trained models so they bring you more value. As a result, if applied for personalization, tailor-made recommendations may boost the customer experience, leading to more satisfaction and better sales. Plus, a gen AI-based chatbot can instantly be in touch with customers freeing up human customer service employees.

• Multiple output types — Generative AI is capable of solving a wide range of problems, and some models are multimodal, meaning that you can receive outputs in various formats apart from text like an image or an audio or video file. This can be handy when brainstorming, seeking inspiration, or looking for answers to complex questions.

• Effortless enhancements — If your system is closely tied to LLMs provided by the big players like OpenAI, each time they make improvements to the LLM, your product automatically improves too without any input whatsoever on your end.

The four bullet points above emphasize the benefits a company can get as a user. But, likewise, becoming a provider of an AI solution and building gen AI applications of your own can also have gains.

By the way, Upsilon has been interviewing many aspiring entrepreneurs and startup founders. Our collection of Startup Stories has many inspiring interviews with founders who are currently building AI products or integrating AI into their solutions. They share insights about their journey, noting the ups and downs and challenges, give first-hand tips on how to build AI apps, and tell about the lessons they've learned while launching and growing their business.

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Is It Worth Building Generative AI Applications in 2025 and Beyond?

With the overall startup failure rate being extremely high over the past few years, entrepreneurs wonder whether getting started with generative AI is worth a shot. Artificial intelligence app development has definitely been trending. As we've mentioned, AI adoption is widespread in lots of industries, and its implementation is only expected to continue growing.

Recent statistics suggest that the generative AI market size is expected to scale the most: from 36 billion USD in 2024 to around 415 billion USD in 2027, and to as much as 184 billion USD in 2030.

What about raising funds? Surely, global monthly average funding has visibly dropped since its peak in 2021: by over 60%. Yet, according to the latest findings, there has been a large interest in the AI sector from investors, in fact, a much bigger interest than in any other sector. Global VCs provide a large share of startup funding to such companies as Musk's xAI, and another big star in the field, OpenAI, wasn't an exception: they recently raised 6.6 billion USD.

While the big players like Anthropic or OpenAI that are behind the development of such core, foundational technology get funding, what about the rest that are just applying these models in a certain industry? Well, the latter category is receiving a lot less funding compared to the aforementioned companies.

Is there room on the market for startups and entrepreneurs thinking about generative AI development? Looks like the future of gen AI is optimistic. It's an auspicious field, provided you create a quality product that brings value and that people actually need, of course. We'll overview how to make generative AI solutions in detail later on on the page.

Generative AI How It Works

How does generative AI work? To create new content, it utilizes sophisticated machine learning methods, including AI-powered learning tools, and more precisely, deep learning and neural networks. Gen AI identifies patterns in existing data and mimics what it found when making outputs.

The process typically begins with collecting large datasets and preparing them. These may include text, audio files, or other relevant materials. The higher the quality of the datasets and the bigger their variety, the more effective generative AI is.

Either way, you'll need vast datasets to train LLMs and other deep learning architectures that gen AI utilizes. In fact, recent large language models are developed with billions of parameters for efficient training. Nonetheless, training LLMs and AI models could get extremely expensive. How much did it cost to train ChatGPT? Resources suggest that OpenAI spent over 100 million USD to train ChatGPT-4 compared to only 2 to 4 million USD that were spent on ChatGPT-3 creation.

What happens next? The AI algorithm then analyzes these samples while it learns and remembers specific patterns. It later uses this obtained knowledge when it makes new outputs following a similar style.

For instance, these models try to get the hang of human languages, diving into the peculiarities of style and context. By learning such intricacies GPT-4 or similar linguistic models allow tools like ChatGPT to produce written content that's contextually relevant, yet can be delivered in different forms like a general conversation with a friend, a brief outline, or in the style of a technical document. Gen AI development that deals with image generation is somewhat similar, but it heavily relies on detailed prompts.

All the big names from ChatGPT to DALL-E use neural network architectures to create content that resembles what humans make. As the models go through the training phase, they learn from the data and the patterns. The parameters are continuously tweaked to make the quality of the outputs better and with fewer mistakes. Human feedback also plays an important role. Here are a few key types to know about when learning how to build gen AI applications.

Transformers

Transformers mainly deal with sequential data and are very handy with natural language processing (NLP), chatbots, and text generation. They are the foundation of numerous NLP models, including BERT and GPT. Thanks to the attention mechanism that helps them process and generate sequential data, they can assess which parts of the input data have the most importance. This, for instance, helps them understand context and the relationships between words (e.g., which words matter most in a sentence), leading to more contextually relevant outputs.

Diffusion Networks

Both diffusion and transformer networks are at the forefront of advancing generative AI systems. The former introduces noise to their source and then reverses the process to create new, realistic outputs. This is especially applicable in creating images or other data types. For instance, new images are generated by denoising image samples and reconstructing data effectively.

Generative Adversarial Networks (GANs)

Such deep-learning models are made up of two neural networks: generators craft new data and discriminators evaluate the outputs with original data to ascertain that they're genuine and authentic. Then, they're refined until they resemble the original samples, which can be highly realistic with images or music.

Variational Autoencoders (VAEs)

This kind of neural network learns to encode input data used for training into a compressed representation. It then serves as the basis on which VAEs decode back when making new samples or unique outputs. Optimizing data reconstruction is a focus, yet, making sure the underlying data structure is meaningful is important too. This can be applicable in cases when you need variations of existing data, say, for sounds, melodies, or images.

Recurrent Neural Networks (RNNs)

RNNs also handle sequential data by remembering previous inputs. They are important for speech recognition or language modeling, where the order of data matters. Utilizing backpropagation over time lets them spot sequences, temporal dependencies, and relationships in ordered data. This is why they're good at generating sequences, which are applied for creating text and audio.

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Foundation Models Used in Generative AI

What are foundation models? These large AI systems are essential as they serve as a starting point or base for building generative AI solutions. In essence, they are like a versatile toolkit or core on top of which developers can create tailored, more complex solutions such as those for image editing or sentiment analysis.

These powerful models are trained on vast amounts of data and learn patterns in order to perform a wide range of versatile tasks. As a result, such machine learning models help create unique content like images, new or summarized text, or participate in conversations like chatbots do. Letэs overview a few commonly applied LLMs to learn more about how to create gen AI solutions.

What are the most well-known large language models with broad adoption?

• GPT (Generative Pre-trained Transformer by OpenAI, a pioneer in the field that appeared in 2018)
  
  
• DALL-E (a GPT architecture variation developed by OpenAI in 2021, it's mostly used for image generation)
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• Llama (released in 2023 by Meta, ex-Facebook, this model focuses on accessibility and efficiency, it's an alternative to GPT and Claude)
  
  
• Claude (developed by Anthropic in 2023, it emphasizes ethical AI use and safety)
  
  
• Stable Diffusion (an open-source model created by Stability AI i n2022 that's also used for image generation similar to DALL-E in game design and other solutions)
  
  
• Gemini (introduced in 2023 by Google DeepMind, it powers many Google products)
  
  
• PaLM (Pathways Language Model by Google, places focus on effective AI training and model scaling, it powers Google Translate and other products)
  
  
• Mistral AI (a rather new model emerging in the industry as an alternative to Claude and GPT)

Some of these foundation models are open-source (such as Meta's Llama), attracting developers with their accessibility. They commonly serve as a base for content creation solutions, code generators, summarization tools, language translation, chatbots, search enhancement, image creation or image classification tools, and other solutions for creative tasks.

They are principally based on prompts and the meaningful information described in them to generate an output. For example, a prompt like "Please provide a list of the best movies with Keanu Reeves" could expect a reply of 10 selected films with a good rating starring or featuring the actor.

Some rely on task-specific instructions together with a labeled dataset. Others place additional focus on retrieving information from external sources like databases, libraries, documents, or even the internet, then convert the stored information into a format called a vector to look up, search, and find what's necessary faster, and then formulate replies by extracting real-time information.

The models could afterward be improved according to the given positive or negative feedback, which can help it give more topically relevant answers next time. It doesn't update the weights in real-time, though, usually such extra training can happen as a separate phase.

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Key Components of Generative AI

Developing generative AI applications implies a few interconnected components. Let's overview the main generative AI components commonly present in apps.

The foundation model like GPT-4, Llama, or others we discussed in the previous section of the article, lies at the heart of a generative AI application's architecture. The web or mobile application's front-end part interacts and communicates with the foundation model via a user-facing application programming interface (API). The API may be a managed service (such as one provided by AWS) or a self-hosted service.

If you're using retrieval-augmented generation (RAG), you'll need additional components. For instance, the text embedding endpoint is necessary for converting text into a format the model can comprehend. Moreover, a vector database is also required since this technique stores and retrieves data from a library or database in order to function (here's a handy vector database comparison). All the aforementioned components are linked together with the help of various developer tools that form the framework for generative AI application development.

If the foundation model requires extra training, customization, or fine-tuning, some more components may be applied. As such, data pre-processing and annotation components can be used to correctly label data for training. Optionally, you might need an ML platform for running the training on appropriate computing instances (unless you're using a solution with API-based fine-tuning). Plus, integrating components for monitoring as well as security tools is essential to ensure smooth performance, protected data, and overall high quality.

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How to Develop a Generative AI App: Step-by-Step Guide

Where should you begin if you want to make a gen-AI-powered app? Let's learn how to get started with generative AI.

Step 1: Project Preparation

Just as with any development project, you have to decide what you're building and why. While you're getting started with generative AI, define the problem. Let's pretend you want to build a chatbot that's supposed to give personalized movie recommendations.

• Who faces this issue?
• Does the problem truly exist?
• Why do you need this solution?
• What challenges should it tackle?
• Which language(s) should it support? 

After lining out the target audience that has this problem, writing out a product problem statement, and noting which solution you have in mind, it is also important to define your goals and objectives.

• What are you trying to achieve? 
• Which outputs are desired?
• Which metrics and KPIs can help you determine whether you're succeeding?

Step 2: Gather a Test Dataset

What most aim for is to create such a solution that combines the broad knowledge of a foundation model with the domain expertise of your niche. If this is the case, you can use a foundation model that best suits your needs that was trained on huge amounts of general data and fine-tune it to perform tasks in your specific domain.

But your AI model, regardless of how intricate it is, won't be able to learn well if you don't have quality data. This means that you need to collect and prepare enough data for the AI to function effectively. To do that, you'll need to provide it with a task-specific dataset, so start by collecting and preparing datasets. You'll need lots of relevant data of high quality, both structured and unstructured, to measure AI effectiveness. And quality may be much more important than quantity at this point.

It is best to apply data preprocessing techniques before utilizing it in the AI model like:

• tokenization;
• categorization;
• splitting;
• labeling;
• standardization;
• proper formatting;
• and others.

Mind how well-structured and properly formatted data is when forming the initial datasets for your AI app. It shouldn't have missing values, errors, or mistakes in labels. You can even browse data exchanges to find or mine relevant datasets that will be specific to your use case.

Step 3: Select the Foundation Model

You'll need to then choose a suitable LLM provider that'll be most relevant for your solution. For instance, to create a chatbot that generates personalized movie recommendations, you can consider OpenAI. Yet it also makes sense to look through what other providers have on offer to compare the existing solutions like using this comparison of LLMs. Check what suits your budget and has decent results in your field. Mind factors like the:

• model's capabilities;
• dataset compatibility;
• availability;
• pricing;
• API documentation;
• computational requirements;
• and others. 

Likewise, to strike a balance between the output quality and the associated costs, keep an eye on the platform's parameters like the maximum number of tokens and other limits like RPM and RPD. For instance, various GPT-4 model versions may have different token limits (e.g., 10k or 40k TPM, tokens per minute), and if you exceed the rate limit on API requests, you can wencounter API errors or face temporary restrictions blocking access. In this case, you'll likely need to pay more to increase your rate limit.

Step 4: Set Up the Environment

At this point, you can choose the necessary tools, and tech stack to build the app. You'll need to set up a development environment, create and export an API key, and handle necessary library installation. For example, if you want to use OpenAI in a server-side JavaScript environment like Node.js, you might need to use an official SDK for JavaScript and Typescript, which can be installed with the help of npm or an analogous package manager.

What about the specific gen AI tech stack? The best-match frameworks and algorithms will entirely depend on the foundation model you opt for. I.e., the framework choice will be completely different, say, if you go for Llama or OpenAI. Yet, LlamaIndex, LangChain, and Vercel are a few popular options.

Step 5: Create an AI Pipeline

Now that you've chosen the appropriate model, you'll need to build a pipeline around it. An AI pipeline is a structured sequence of processes that you have to create in order to transform input data into outputs. In essence, it's a series of steps or tasks that an input has to go through. For example, you can upload a PDF, convert it into text format, send both the text and the prompt to the LLM, and save the result.

Although the LLM will do most of the heavy lifting, it'll still need human assistance to handle some parts of the process. You'll need to modify the model, adjust the parameters, update the prompt, and add regularizations to make sure the model delivers new or previously unseen data in outputs.

Essentially, you have to model the steps that'll let you train and deploy it, so proceed with designing the flow. For instance, what should the conversation be like between a user and your chatbot that gives movie recommendations? Plan out the sample questions that'll let you understand the user preferences and how the recommendations from the bot should be displayed.

Prompt engineering is generally handled at this point, too. You have to come up with requests that'll be sent to the LLM like: "Provide a list of the top 5 movies about {topic name} with a 5-star rating or higher. Ignore a, b, c sources. Return data in the following format..."

Step 6: Fine-Tune the Model with Your Domain Data

As mentioned before, you'll need business-specific datasets to fine-tune your AI. You can evaluate the model's performance and improve data accuracy using scoring and other tactics.

Next, you'll have to put the app together. Use a web framework to build the app, which will allow you to create a user interface tied to the backend logic.

Then, integrate the LLM into the app. Falling back on the chatbot example, you'll then indicate the prompts, which will deliver personalized recommendations on films. After the recommendations are displayed, the embedded LLM and created chatbot can later be refined according to the received user feedback. You can also use a validation set to test the model's performance and enhance it if necessary.

Step 7: Release, Monitor, and Improve

Lastly, when the application is well-tested and debugged, it's ready for deployment. You'll need to work on infrastructure setup (be it cloud-based or hardware), use containerization tools, and other peculiarities.

Additionally, you may handle monitoring settings that'll let you monitor the app's performance and how users interact with it. This is done via performance metrics, feedback loops, detecting drift, and other methods aimed at allocating areas for improvement.

How Upsilon Approaches Generative AI App Development

Which best practices does Upsilon's team use when we work on generative AI software development? Here are some good-to-knows and expert tips shared by our CEO, Andrew Fan, and CTO, Nikita Gusarov ⤵

Data Preparation for AI Training

What can't AI software development do without? Data, of course. Upsilon's CEO, Andrew Fan, notes the importance of preparing data for AI projects in advance.

"Let's assume you'd like to work on a project that'll allow you to take a photo of a paper receipt and automatically transfer the information into your CRM or bookkeeping system with the help of AI. To bring this to life, you'll need to have lots of examples of various receipt images that could possibly be used within your system, or at least know where to find many of them to train your AI. You also have to be certain about which data you want pulled from each receipt, as without this combo, you won't get far."

So it's not just about how to develop generative AI software, it's a lot about how to gather the most relevant data as early on as you can. The technical side may get rather complicated, however, without enough quality data from the outset, it'll be very difficult to train the AI to be effective at delivering what you expect.

Robust Gen AI Testing Pipeline and Metrics

What is another fundamental step of early gen AI software development? Working on a solid AI testing pipeline and choosing your metrics. Here's what Upsilon's CTO, Nikita Gusarov, points out regarding how to build a generative AI application the right way:

"When you're building a generative AI system, you should start with the AI testing pipeline. Way too often, teams make the critical mistake of attempting to improve their AI without using data and numbers for backup. You'd definitely want to avoid this. To do it the right way, you must have proper AI metrics to evaluate the quality of your AI. You can then try to enhance the AI, yet you have to collect data and compare it to the previous metrics to draw realistic conclusions. If the metrics improve, you're on the right track. But without meaningful AI metrics, all your improvements could be a complete waste of time and effort."

Nikita also gave an example describing the process if we hypothetically want to classify documents using ChatGPT. Let's say that we have various types of documents like invoices, tax declarations, and so on. You're aware of which types should be assigned to specific sets of documents. But how do you assess how well your classification works? One way to go is using the Confusion Matrix, which means that to test precision you'll need to calculate:

• how many documents were classified as a certain document type correctly (true positive);
• how many documents were mistakenly classified as a certain document type (false positive);
• how many documents weren't classified as a certain document type at all by mistake (false negative).

Based on these three figures, you'll calculate the fourth measure called the F-score. This procedure has to be done for each available document type, and as a result, you'll get a number showing how well the classification works for each type. Then, calculate the arithmetic mean value (or some other average of all measures) to get a single number from 0 to 1. The higher the number, the better the performance of the classification. This way, you can improve the AI using a numerical indicator as guidance, instead of proceeding blindly.

MVP Development for AI Products

How about developing minimum viable products? Upsilon has been building MVPs for over a decade now, including MVPs with generative AI, so here are a few points our CEO and CTO believe are worth noting in this respect.

"If you're planning to build a SaaS MVP, in most cases, it makes sense to focus on solving specific user problems using large language models (LLMs) that already exist. A good example of such gen AI algorithms is ChatGPT which uses large data sets and deep learning.

Why is attempting to build your own robust AI for your MVP most likely going to be a mistake? For one thing, there's a current tendency for all LLMs to become a commodity. Today the best one might be ChatGPT, so you can take advantage of it and save time. In a few years, it could be something else you can switch to."

With MVPs, your main aim is to hit the market with a quality working product as soon as you can, even if it has limited functionality. Therefore, when creating an early version of your product, it is wise to give feature prioritization due thought as well as carefully consider where you can cut corners in terms of development.

Major Takeaways on Generative AI Software Development

There, now you know more about how to create generative AI solutions. In general, AI has a promising future. VC interest in the sector as well as the market forecasts for the upcoming years are rather inspiring, which explains why many entrepreneurs are hurrying to jump on the gen AI train. Not to mention the many benefits it can bring if used to enhance an existing product.

If you have a great gen AI-based application idea but need some assistance with the tech side, Upsilon will be glad to lend a hand with generative AI implementation. Our expert team has been providing MVP development services for over a decade, and we've helped bring numerous AI products to life. So, feel free to reach out to discuss your needs!