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How to Build an App with ChatGPT

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Artificial intelligence has been an essential driver of innovation and efficiency in various fields. It has enabled the development of software models that have fulfilled numerous purposes and helped accomplish meaningful work. Different kinds of AI models are gradually replacing human labor, accomplishing tasks that were once only possible with human effort and making processes more streamlined and efficient.

One of the most exciting developments in AI is conversational AI, which seeks to improve communication between people and machines. ChatGPT is an AI-based chatbot that has gained widespread popularity and is considered a leading example of conversational AI technology. It provides a powerful platform that allows users to harness the potential of conversational AI, blurring the lines between humans and machines.

As new AI advancements, such as conversational AI and generative AI, continue to be developed, ChatGPT has become a topic of interest for technologists, businesses, and the society at large. ChatGPT offers a variety of benefits, including instant access to vast knowledge, versatile assistance in daily tasks, and a supportive presence for users. With continuous improvement and accessibility features, it has become an indispensable tool in our modern world. While ChatGPT exhibits a remarkable level of finesse in handling various tasks, it is its powerful coding capabilities that have captured the world’s attention. By harnessing its advanced coding capabilities and versatile task handling, developers can streamline their workflows, save time, and create innovative applications with remarkable efficiency. With ChatGPT’s assistance, the complex and time-consuming aspects of app development become more manageable, empowering developers to focus on creativity and enhancing user experiences.

In this article, we will deeply understand this powerful AI-based chatbot and how we can build an app with it.

Introduction to ChatGPT

ChatGPT is an artificial intelligence language model developed by OpenAI. Essentially, it’s built on the bedrock of Large Language Models (LLMs), a highly sophisticated breed of machine learning models proficient in comprehending and generating human language.

The essence of ChatGPT lies in its acronym, GPT, which stands for Generative Pre-trained Transformer, describing its fundamental attributes:

  • Generative: This signifies the capacity of GPT models to conceive new, context-driven content. These models are adept at creating text that mirrors human conversation, both in relevance and coherence.
  • Pre-trained: The models are trained using a massive array of text data encompassing a myriad of sources. This diverse linguistic training enables them to grasp intricate patterns, contexts, and a host of facts, laying a robust foundation for high-quality text generation.
  • Transformer: Transformer is the underlying architecture of GPT models. These models, based on Transformer architecture, leverage self-attention mechanisms and parallel computation to manage extensive language tasks efficiently and generate text with exceptional, contextual accuracy.

The genesis of ChatGPT goes back to 2018, with OpenAI’s inaugural GPT language model. This model’s ability to emulate human responses paved the way for the development of ChatGPT. The GPT series commenced with GPT-1, a promising yet nascent language model. It was in 2019 that GPT-2 emerged, showcasing a significant leap in language comprehension and generation. However, the real game-changer was GPT-3, released in 2020, causing a paradigm shift in the generative AI space with its unrivaled prowess. This success led to the fine-tuning of GPT-3 into GPT-3.5, an upgraded version of ChatGPT that is now freely available on OpenAI’s platform. ChatGPT was officially launched by OpenAI in November 2022, garnering instant popularity. Leveraging the success of GPT-3.5, OpenAI unveiled GPT-4, a subsequent iteration that offered substantial enhancements to ChatGPT’s performance, scalability, and overall capabilities.

ChatGPT, with its AI-fueled natural language processing prowess, interprets and formulates text in response to user prompts. Its applications are expansive, from answering queries to assisting with content creation, language translation, and more. OpenAI employed human AI trainers to fine-tune these language models, leveraging reinforcement learning and human feedback to amplify the user experience. Consequently, ChatGPT delivers precise, timely, and contextually apt responses to a plethora of user queries. Given its superior text-generating capabilities, ChatGPT finds utility across diverse industries and business use cases, such as customer service, content creation, and programming assistance. It manifests the transformative potential of AI in reshaping our daily lives and professional environments.

The evolutionary journey of ChatGPT, from GPT-3 to the advanced GPT-3.5 and more recently GPT-4, reflects the fast-paced advancements in generative AI research and development.

How does ChatGPT work?

ChatGPT is an advanced variant of a category of machine learning models used in Natural Language Processing (NLP) known as Large Language Models (LLMs). LLMs process massive volumes of textual data and discern the connections among words in the given text. These models have seen significant growth with the recent progress in computational capabilities. The effectiveness of LLMs enhances with the expansion of their input datasets and the broadening of their parameter space.

While GPT-3, the foundation model behind ChatGPT, made substantial strides in the field of natural language processing, it falls short in accurately aligning with user intentions. For instance, GPT-3 might generate outputs that:

  • Are unhelpful and fail to adhere to the user’s explicit commands.
  • Produce ‘hallucinations’ or imaginary claims that represent false or non-existent facts.
  • Lack of interpretability, thus making it challenging for humans to comprehend the reasoning behind a specific decision or prediction made by the model.
  • Generate toxic or biased content, leading to harmful or offensive outputs and the dissemination of misinformation.

ChatGPT introduced advanced training methods to mitigate some of these inherent issues typically found in standard Large Language Models. ChatGPT leverages fine-tuning, Reinforcement Learning from Human Feedback (RLHF), embeddings, reward modeling, and Proximal Policy Optimization (PPO) to provide high-quality, contextually relevant responses.

How does ChatGPT work


Embeddings play a crucial role in helping ChatGPT understand and generate contextually relevant responses. and are used in its pre-training phase. Embeddings are vector representations of words, phrases, or sentences that capture the semantic and syntactic relationships among them. They enable the model to process and generate human-like language.

During the pretraining phase, ChatGPT learns embeddings from a vast corpus of text data. The model captures the contextual information and relationships between words, phrases and sentences in a high-dimensional space. These embeddings help ChatGPT comprehend the input data’s meaning, nuances and dependencies. When a prompt is given to ChatGPT, the model uses the learned embeddings to understand the context and relationships between the words in the prompt. It then generates a contextually relevant response by finding the embeddings that best match the input’s semantics and syntax.

Embeddings enable ChatGPT to generate coherent and contextually appropriate responses and allow the model to generalize its knowledge to new, unseen inputs. This is possible because the embeddings capture the underlying structure and patterns in the language, enabling the model to adapt its responses to a wide range of prompts. At this stage, the model gains a generalized understanding of language.


ChatGPT is a derivative of InstructGPT, which brought forward an innovative method of integrating human feedback into the training procedure to better synchronize the model’s outputs with users’ objectives. The technique, known as Reinforcement Learning from Human Feedback (RLHF), is comprehensively detailed in OpenAI’s 2022 publication, “Training language models to follow instructions with human feedback.” Here’s a simplified explanation of the process.

Once the pretraining phase is complete, fine-tuning comes into play. ChatGPT is further trained on a smaller, carefully curated dataset tailored to the target domain or task. This dataset consists of input-output pairs generated by human labelers who provide the expected responses to a list of prompts.

ChatGPT’s parameters are updated during fine-tuning to better align with the target task or domain. This process allows the model to generate more accurate, relevant and useful responses to the specific context or problem at hand. Fine-tuning adjusts the general language skills of ChatGPT acquired from pretraining to fit the particular demands of a desired task.

The fine-tuning phase may also involve techniques such as Reinforcement Learning from Human Feedback (RLHF), which helps minimize harmful, untruthful, or biased outputs. This technique involves a combination of supervised fine-tuning, reward modeling, and Proximal Policy Optimization (PPO). RLHF aims to optimize the model’s responses based on human preferences, ensuring that the model generates text that is accurate, user-friendly and aligned with the desired output.

There are several stages used for fine-tuning the GPT3 model. Here is a detailed overview:

Step 1: Supervised Fine Tuning (SFT) model

The initial stage of developing ChatGPT entailed the Supervised Fine Tuning (SFT) of the GPT-3 model. A team of 40 contractors built a supervised training dataset having inputs with corresponding known outputs. The inputs or prompts were sourced from real user queries submitted to the OpenAI API. The contractors then composed suitable responses to these prompts, thus creating a dataset with known input-output pairs. Utilizing this dataset, GPT-3 was fine-tuned to create the SFT model, also referred to as GPT-3.5.

The dataset was designed to maximize diversity in prompts. To achieve this, a cap of 200 prompts was placed on each user ID, and prompts that had lengthy common prefixes were omitted. Further, any prompts containing Personally Identifiable Information (PII) were excluded to safeguard user privacy.

In addition to gathering prompts from the OpenAI API, the contractors were tasked with generating sample prompts to augment categories that lacked substantial real data. The targeted categories included:

  • Plain prompts: These are general queries without any specific context.
  • Few-shot prompts: These are instructions that contain multiple query-response pairs.
  • User-based prompts: These correspond to specific use cases requested through the OpenAI API.

The contractors strived to decipher the user’s intended instruction when formulating responses. The paper outlining the process highlighted three main ways prompts seek information:

  • Direct: Prompts like “Tell me about…” ask for information directly.
  • Few-shot: These prompts provide two examples on a topic and ask for another example.
  • Continuation: Here, the prompts start a story and ask the model to complete it.

The amalgamation of prompts from the OpenAI API and those created by contractors produced a dataset of 13,000 input/output pairs, providing a solid foundation for the supervised model.

Step 2: Reward model

After the SFT model’s training as described in the first step, it starts to produce responses that align more closely with user prompts. The next level of refinement involves the implementation of a reward model. This model uses a sequence of prompts and responses as inputs and outputs a scalar value termed as a ‘reward’. The reward model plays a pivotal role in applying Reinforcement Learning, where the model learns to generate outputs that maximize the reward (as elaborated in step 3).

For the reward model’s training, labelers were shown 4 to 9 outputs from the SFT model for a single input prompt. Their task was to rank these outputs in order of quality, from the best to the worst, thus generating a variety of output ranking combinations. However, treating each combination as a separate data point in the model resulted in overfitting, which is the model’s inability to extrapolate beyond the data it has seen.

To rectify this, each group of rankings was treated as a single batch data point when building the model. This approach ensured that the model could generalize better and avoid overfitting.

Step 3: Reinforcement Learning (RL) model

The final phase involves the application of a Reinforcement Learning (RL) model. In this phase, the model is further refined using reinforcement learning. The model is given a random prompt, and it responds based on the policy learned during the supervised fine-tuning (SFT) step. The policy represents the strategy the model has acquired to maximize its reward. The reward model developed during SFT assigns a scalar reward value to the pair of prompt and response. This reward is then used to guide the RL model in refining its policy further.

Although steps two and three can be repeated iteratively to refine the model further, this has not been extensively implemented in practice.

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Strengthen foothold in the generative AI domain with ChatGPT-like AI-powered apps

Key features of ChatGPT

ChatGPT stands out as a powerful tool in the world of advanced language systems. It has unique qualities that make it different and more effective than older models for a wide range of uses.

Let’s delve into these distinctive characteristics, emphasizing their importance and influence on ChatGPT’s interactive abilities and potential.

Advanced natural language understanding

ChatGPT is known for its ability to comprehend human language, a capability that stems from its foundational large language model. This language model isn’t just about recognizing words but about understanding the intricate relationships between them. It has a comprehensive grasp of language rules such as grammar, syntax, and semantics, enabling it to generate text that is almost indistinguishable from that written by humans.

This advanced linguistic comprehension of ChatGPT isn’t confined to just understanding words or phrases. It also includes understanding the context, the subtleties, the nuances, and the connotations that exist with human language. It’s capable of decoding metaphors, analogies, cultural references, and even humor to a certain extent.

When engaged in a conversation, ChatGPT is designed to interpret a wide array of inputs, whether they are questions, comments, or directives. It doesn’t just process these inputs literally but understands the intention behind them, facilitating a more natural and human-like interaction. It can understand a question, dissect its meaning, and provide an informed response. It can comprehend a comment, gauge its sentiment, and react appropriately. It can interpret a directive, assess its requirements, and execute the task accordingly.

This profound language comprehension, combined with the ability to generate human-like text, makes ChatGPT a versatile tool capable of engaging in meaningful and contextually accurate conversations, thus paving the way for more effective human-AI interactions.

Contextual awareness

ChatGPT’s ability to maintain context from earlier exchanges is a key feature, enabling it to provide responses that are not only relevant but also coherent within the flow of the conversation. However, it’s important to understand that GPT models are subject to a context window limit, dictating the amount of text they can comprehend and keep track of at any given moment.

To illustrate, GPT-3 was designed with a context window capacity of 2048 tokens, which equates to around 2,000–3,000 words depending on the complexity and structure of the language in use. In contrast, OpenAI suggests that GPT-4, the underlying architecture for ChatGPT, is significantly more advanced. It has the capability to interpret and produce up to a whopping 25,000 words of text.

Why does this matter? Contextual awareness is what empowers ChatGPT to perform effectively in prolonged conversations. It helps maintain a coherent narrative and ensures that responses align with the evolving dialogue. Without this feature, the model’s responses could seem disjointed or out of sync with the conversation, diminishing its effectiveness and user experience.

Therefore, this capacity for contextual awareness stands as a critical component of ChatGPT’s functionality, facilitating meaningful and seamless interactions that mirror the flow and continuity of human conversation.

Comprehensive domain knowledge

ChatGPT’s comprehensive domain knowledge is another noteworthy feature that sets it apart. As an AI chatbot, it has been trained on an expansive dataset composed of text from a vast array of sources. This means it is capable of producing responses on a diverse range of subjects, making it feel like you are conversing with a multi-disciplinary expert.

You can converse with ChatGPT on an extensive range of topics, including:

  • Science and technology
  • Arts and humanities
  • Social sciences
  • Mathematics and statistics
  • Medicine and healthcare
  • Business and finance
  • Law and politics
  • Pop culture and entertainment
  • Everyday life
  • Environment and geography

While ChatGPT can engage on an extensive array of topics, the depth and accuracy of its understanding may differ depending on the complexity of the subject matter and the question at hand. Nevertheless, its comprehensive domain knowledge makes it a versatile tool for a wide variety of conversational needs.

Scalability and versatility

ChatGPT exhibits outstanding scalability and reliability, owing to its well-designed architecture and training methodologies. This language model can seamlessly adapt to a wide array of applications and industries, while the fact that it can be fine-tuned enhances its performance and versatility across diverse use cases.

Although quantifying ChatGPT’s scalability and adaptability with statistical figures proves challenging due to various factors like computational resources and infrastructure, certain indicators point towards its capabilities:

  1. Model size: Based on the GPT-4 architecture, ChatGPT boasts an increased number of parameters compared to its predecessor, GPT-3.5. This enhancement enables the model to grasp intricate language patterns and deliver superior performance.
  2. Training data: ChatGPT’s training relies on vast datasets, comprising terabytes of text data from diverse fields. This extensive knowledge base equips the model with comprehensive coverage of numerous subjects.
  3. Computational resources: Managing such massive datasets demands substantial computational power, which is efficiently handled by employing high-performance GPUs or TPUs for intricate mathematical operations during training.
  4. Fine-tuning: Customization for specific tasks involves additional training and reinforcement learning on custom datasets, enabling ChatGPT to adapt seamlessly to varied contexts.

Regarding user interactions, ChatGPT’s scalability primarily hinges on infrastructure and deployment optimizations. Theoretically, it can cater to millions of users with the appropriate hardware and software setup, although the actual numbers depend on specific use cases and available resources.

ChatGPT’s remarkable scalability and reliability emerge from its architecture, fine-tuning capabilities, vast training data, computational efficiency, and adaptability to user interactions. These collective strengths empower ChatGPT to deliver superior performance across numerous domains, making it a highly valuable and versatile language model.

How to build an app with ChatGPT?

Let’s now explore the potential of OpenAI’s ChatGPT in application development. The challenge is to create a straightforward Android application showcasing 50 popular stock market shares and their current prices.

A similar code is available here –

To ensure a true test of ChatGPT’s capabilities, we have laid out the following guidelines:

  • No coding from our end: We will not be writing a single line of code ourselves in this experiment. Instead, our role is to act as the conduit, bringing ChatGPT’s instructions to life.
  • Copy-paste from ChatGPT: Our interactions with ChatGPT will be limited to copying and pasting the code it generates. This means we’ll be relying on the model’s AI capabilities to produce all the code necessary for the application.
  • Implementing ChatGPT’s instructions: We’ll adhere to the chatbot’s directives for any required settings, including navigating through Android Studio’s wizards, obtaining the API key from the service, and so on.
  • Bug handling: Should we encounter any bugs or errors during the development process, we’ll lean on ChatGPT for solutions. We’ll ask the chatbot for guidance and apply the fixes as suggested.

Our goals for this project are straightforward:

  • Compile-ready code: The code generated by ChatGPT should be ready to compile without any errors.
  • Functional application: Once compiled, the application should run smoothly on an Android device.
  • Desired output: Ideally, upon successful launch, the application should display 50 popular stocks along with their current prices.

This experiment aims to showcase the capabilities of ChatGPT and its potential to disrupt traditional application development processes.

Here are the prompts and output details to build an app with ChatGPT:

Prompt – I’m interested in developing an Android application that displays a list of 50 stock prices, utilizing the Yahoo API. Could you guide me on how to accomplish this?

ChatGPT output:

Sure, I’d be happy to guide you through the process of developing an Android application that displays a list of 50 stock prices using the Yahoo API. Here’s a step-by-step guide:

    1. Set up your development environment: Install Android Studio, the official IDE (Integrated Development Environment) for Android app development.
    2. Create a new Android project: Launch Android Studio and create a new Android project. This will provide you with a basic structure for your app, including a default layout file and a Java file for your main activity.
    3. Obtain an API key from Yahoo Finance: Sign up for a free API key from Yahoo Finance. This will allow your app to access the stock price data.
    4. Add necessary dependencies to your project: You’ll need to add some dependencies to your app’s build.gradle file. Here’s an example of what you might need:
dependencies { implementation 'com.squareup.retrofit2:retrofit:2.9.0' implementation 'com.squareup.retrofit2:converter-gson:2.9.0' }

    1. Create a Java interface for the Yahoo Finance API: This interface will define the endpoints of the API and the parameters you need to pass to each endpoint. Here’s an example:
public interface YahooFinanceAPI {
    String BASE_URL = "";
    Call getStockPrices(@Query("apikey") String apiKey, @Query("symbol") String symbol);

    1. Use Retrofit to make HTTP requests to the Yahoo Finance API: Retrofit is a library that simplifies the process of making HTTP requests in Android. Use it to create an instance of your YahooFinanceAPI interface and make requests to the API:
Retrofit retrofit = new Retrofit.Builder()
YahooFinanceAPI yahooFinanceAPI = retrofit.create(YahooFinanceAPI.class);

    1. Fetch stock prices and display them in a list: Make a request to the API to fetch the stock prices, then parse the response and display the stock prices in a RecyclerView.
Call call = yahooFinanceAPI.getStockPrices(YahooFinanceAPI.API_KEY, "symbol");
call.enqueue(new Callback() {
    public void onResponse(Call call, Response response) {
        if (response.isSuccessful()) {
            // Extract stock data from response and add to RecyclerView adapter
    public void onFailure(Call call, Throwable t) {
        // Handle the failure

  1. Test your app: Finally, run your app on an emulator or a physical device to test it. Make sure it correctly fetches and displays the stock prices.

Remember to replace “YOUR_API_KEY_HERE” with your actual API key from Yahoo Finance, and “symbol” with the symbols of the stocks you want to fetch. Please note that the Yahoo Finance API may have changed after my training data, so you may need to check the latest API documentation for the most accurate information.

Prompt: Provide me complete class with error handling

ChatGPT response: Sure, here’s how you can implement the MainActivity class with error handling:

import android.os.Bundle;
import android.widget.Toast;
import androidx.recyclerview.widget.LinearLayoutManager;
import androidx.recyclerview.widget.RecyclerView;
import java.util.ArrayList;
import java.util.List;
import retrofit2.Call;
import retrofit2.Callback;
import retrofit2.Response;
import retrofit2.Retrofit;
import retrofit2.converter.gson.GsonConverterFactory;
public class MainActivity extends AppCompatActivity {
    private RecyclerView recyclerView;
    private StockAdapter adapter;
    private List stockList;
    protected void onCreate(Bundle savedInstanceState) {
        recyclerView = findViewById(;
        stockList = new ArrayList<>();
        adapter = new StockAdapter(this, stockList);
        RecyclerView.LayoutManager layoutManager = new LinearLayoutManager(this);
        Retrofit retrofit = new Retrofit.Builder()
        YahooFinanceAPI yahooFinanceAPI = retrofit.create(YahooFinanceAPI.class);
        Call call = yahooFinanceAPI.getStockPrices(YahooFinanceAPI.API_KEY, "AAPL,GOOG,MSFT");
        call.enqueue(new Callback() {
            public void onResponse(Call call, Response response) {
                if (response.isSuccessful() && response.body() != null) {
                } else {
                    Toast.makeText(MainActivity.this, "An error occurred while fetching data", Toast.LENGTH_LONG).show();
            public void onFailure(Call call, Throwable t) {
                Toast.makeText(MainActivity.this, "Failed to fetch data: " + t.getMessage(), Toast.LENGTH_LONG).show();

In this implementation, when the API request is successful and the response body is not null, it clears the existing stock list, adds the new stocks from the response, and notifies the adapter that the data has changed. If the request is unsuccessful or the response body is null, it displays a toast message indicating an error occurred. If the request fails entirely, it displays a toast message with the failure message.

Please replace “AAPL,GOOG,MSFT” with the symbols of the 50 stocks you are interested in. You would also need to replace YOUR_API_KEY_HERE with your actual API key.

Please note, the Yahoo Finance API may not support this directly, this code is a simple mockup to provide a general direction. You might need to adjust the code to fit the actual API you are using.

Also, you should replace the StockAdapter and Stock with your own implementation. The StockAdapter should be a RecyclerView.Adapter subclass that knows how to display your Stock objects, and Stock should be a class that represents the data for a single stock.


Should you encounter difficulties with any of the steps mentioned above, ChatGPT is available to provide comprehensive guidance. For instance, if you are uncertain about how to initiate a new Android project in Android Studio, pose the question to ChatGPT, and it will lead you through the process with clear, step-by-step instructions.

Discuss your project with LeewayHertz’s AI experts

Strengthen foothold in the generative AI domain with ChatGPT-like AI-powered apps

Factors to consider while building an app with ChatGPT

Current generative AI models such as ChatGPT come with certain shortfalls which need to be addressed properly to tread carefully while building an app with ChatGPT. Here are some of them:

Removing bias and toxicity

As much as we value the power of generative AI, we must acknowledge that with great power comes great responsibility. It’s important to recognize that these models are trained on data derived from the vast and often unpredictable expanse of the internet. As a result, they can contain deeply ingrained biases and toxic language that may inadvertently surface in the final product. That’s why responsible AI practices are more crucial than ever before. We must ensure that our AI models are developed and deployed ethically and socially responsibly. By prioritizing responsible AI practices, we can help mitigate the risks of biased and toxic content while harnessing the full potential of generative AI to bring about a better world. Additionally, to ensure that the output generated by AI models is free from bias and toxicity, it’s important to take a proactive approach, including filtering the training datasets to remove any potentially harmful content and implementing additional watchdog models to monitor the output in real-time. Furthermore, enterprises can enhance the quality of their AI models by leveraging their own first-party data to train and fine-tune their models. By doing so, they can tailor the output to meet their specific use cases and improve overall performance.

Improving hallucination

While ChatGPT has the ability to generate highly persuasive arguments, it’s important to note that these arguments may not always be factually accurate. In fact, this phenomenon is known as “hallucination” within the developer community, and it can limit the reliability of the output generated by AI models. To address this issue, OpenAI and other vendors are taking a variety of measures, such as data augmentation, adversarial training, improved model architectures, and human evaluation. These steps aim to improve the output’s accuracy and reduce the risk of hallucination. If you are building an app with ChatGPT, it’s important to follow similar measures to ensure that the output generated by the model is as accurate and reliable as possible. By doing so, you can help build user trust in your app and ensure that it delivers high-quality results.

Preventing data leakage

Clear policies should be in place so that it can prohibit developers from entering sensitive information into ChatGPT, which could become incorporated into the model and potentially resurface later in a public context. By instituting these policies, we can help prevent the inadvertent disclosure of sensitive information and protect the privacy and security of individuals and organizations. It’s critical that we remain vigilant in safeguarding against the potential risks associated with the use of AI models like ChatGPT, and that we take proactive measures to mitigate these risks.

Incorporating queries and actions

While current generative models are capable of providing answers based on their initial large training data set or smaller “fine-tuning” data sets, both of which are essentially snapshots of the past, the next generation of models will take things to the next level. These models will have the ability to identify when they need to look something up in external sources such as a database or Google, or when to trigger actions in external systems. This will transform generative models from disconnected oracles to fully connected conversational interfaces to the world. By enabling this new level of connectivity, it is possible to unlock a whole new set of use cases and possibilities for these models allowing a more dynamic and seamless user experience that can provide real-time, relevant information and insights.

So, as we move forward with the development and deployment of generative models, we must keep in mind this exciting potential for integration with external systems and information sources enabling us to create a new generation of AI-powered applications that are more useful, powerful, and impactful than ever before.

Benefits of using ChatGPT for app development

Artificial intelligence and AI-based development tools have emerged as important factors driving progress in contemporary app development. These tools offer diverse capabilities, enabling businesses to achieve their goals with greater efficiency and effectiveness, regardless of their industry. ChatGPT for app development can greatly streamline the development process while enhancing the in-app user experience. In light of this, let’s take a closer look at how this AI-based chatbot is changing app development.

Here’s what you need to know.

Writing codes

App development often requires writing intricate and time-consuming code. However, with the help of ChatGPT, both professionals and beginners in app development can streamline their coding process. Additionally, if developers encounter any issues with their code, they can use the tool to modify or fix them quickly. Furthermore, ChatGPT can be utilized to write code in any programming language.

Improved efficiency

ChatGPT can provide significant benefits for app development companies by streamlining various tasks and processes, providing quick solutions, and allowing developers to focus on more critical responsibilities. This can lead to an increase in efficiency and productivity, resulting in businesses saving time and being able to focus on growth opportunities. This tool can be a powerful asset for companies looking to optimize their operations and drive success.

Debugging codes

As a programmer, you can leverage ChatGPT’s capabilities to your advantage by utilizing it to identify and troubleshoot errors in your code. By requesting ChatGPT to review a piece of code for mistakes, it will not only point out the issues but also provide suggestions on how to fix them, including specific examples.

ChatGPT’s code generation capabilities ensure that the resulting code is:

  • Error-free and bug-free
  • Clearly and thoroughly documented
  • Simple to comprehend and maintain
  • Tailored to meet specific business requirements
  • Built to perform well in the production environment


Leveraging the capabilities of ChatGPT to build an application is an innovative and powerful approach to software development. As we have demonstrated throughout this article, the process is feasible and remarkably efficient. With ChatGPT’s advanced natural language understanding, comprehensive domain knowledge, and contextual awareness, you can create engaging, user-friendly, and highly functional applications.

Moreover, the fact that ChatGPT can guide you through every step of the way, from initial planning to error handling, makes it an invaluable tool for seasoned developers and novices alike. No matter your level of programming expertise, ChatGPT is there to assist you, streamlining the development process and making app building more accessible than ever.

What sets ChatGPT apart is its ability to build applications and its potential in how we approach software development. By leveraging this artificial intelligence tool, you can accelerate your development timelines, optimize your code, and, ultimately, create better applications.

Embrace the future of software development with ChatGPT. It’s more than just a tool, it’s a game-changer! You can bring your ideas to life with greater ease, faster speed, and superior quality, all with the power of ChatGPT at your fingertips. So, take the plunge and let ChatGPT guide you in creating the next big thing in the app world. The journey is just as rewarding as the destination.

If you want to build or integrate ChatGPT-like solutions into your business processes, contact LeewayHertz’s ChatGPT developers.

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Author’s Bio


Akash Takyar

Akash Takyar LinkedIn
CEO LeewayHertz
Akash Takyar is the founder and CEO of LeewayHertz. With a proven track record of conceptualizing and architecting 100+ user-centric and scalable solutions for startups and enterprises, he brings a deep understanding of both technical and user experience aspects.
Akash's ability to build enterprise-grade technology solutions has garnered the trust of over 30 Fortune 500 companies, including Siemens, 3M, P&G, and Hershey's. Akash is an early adopter of new technology, a passionate technology enthusiast, and an investor in AI and IoT startups.

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