Balancing AI and Human Expertise in Software Development: Eastern Peak’s Best Practices

Recent progress in artificial intelligence (AI) and machine learning (ML) has opened up many new opportunities for software development. It presents new ways for innovative and unique solutions worth exploring. The latest technologies can speed up work processes, enhancing the quality of the outcomes while decreasing the costs and improving the desired results.

In the IT industry, keeping pace with the times is more important than in most other niches. That’s why our team at Eastern Peak has already started to test and utilize the AI potential for software development. For now, we kept our focus on tools like ChatGPT and similar AI tech to explore what such technology can do for software projects.

In this article, we will go through our observations and experiences with AI in software development. We’ll cover what we have tested so far and how these tools help ensure optimal solutions to our clients’ needs.

Understanding the limitations of AI and associated risks

As with any new technology on the market, ChatGPT and similar language models have their benefits and pitfalls. Knowing all the potential risks and limitations will help developers utilize given tools in the most beneficial way.

So let’s outline the three main AI language models’ limitations when applied to software development:

  1. Lack of flexibility: ChatGPT and other similar tools create code by following certain patterns in the input data. This can limit their ability to create code that is customized for specific needs and unusual situations, especially in complex projects. Thus, it is not as nuanced or flexible as desired.
  2. Debugging complications: AI language model is still capable of making mistakes. When it happens, finding the roots of the problem becomes a challenge, making debugging a time-consuming task.
  3. Limited customization: Language models are not as experienced with nuanced coding or software development as professionals. It means that they are not suited for custom solutions or complex programming of narrow-niche projects. Hence, developers can’t turn to AI models with specific requests just yet.


The given limitations can cause significant issues with the overall quality of the final product.  That is why developers should not solely rely on language models in the development process. The AI assistance must be always monitored by experts in the area and corrected when needed. For now, human interference in language models’ work is an essential element of productive cooperation.

Other risks of heavy reliance on language models include:

  1. Drop in quality. The language model is as good as it was thought to be. In other words, the success of the final product will depend on how well the model understands the assignments and the required actions. Its experience and training in the area will determine the final quality of the product. The lack of such may result in severe issues and bugs.
  2. Lack of transparency. With the given technology, users don’t see the middle stages of any process. We only receive the final results without knowing the chain of actions leading to that moment. Hence, developers have to deal with a lack of transparency, which affects the process of editing, debugging, and introducing improvements.
  3. Excessive dependence. So far, no AI tool can be a reliable substitute for real-life professionals. They are not trained or sophisticated enough to replace professional expertise. Yet, an overreliance on these tools can lead to a reduction in work creativity and other necessary skills required for professional growth. Such dependence can even lead to a drop in the overall industry crisis.
  4. Security compromises. Lastly, the security and reliability of AI-generated codes are still under big question. In some cases, such codes can introduce unnecessary safety risks due to undesirable vulnerabilities. Any code weaknesses eventually lead to serious safety risks, including cyber-attacks.


Of course, the risks above are only obvious indications of AI-powered software development tools’ limitations at the moment. They demonstrate the necessity of heavy monitoring of AI models’ whenever it is being incorporated into the development process. These risks also serve as a reminder to carefully test and analyze the received code after each session.

Identifying the right use cases of AI technology

Considering the risks and limitations of AI models, it is possible to find the best uses for the given technology. For instance, the projects’ size, complexity, and urgency must be taken into account before considering using AI technology in the development process. The level of expertise and experience of the software developers’ team also plays a role in successful AI tool implementation.

Here, at Eastern Peak, we run a full analysis of each new project to determine whether the AI language model can be of use, where it can be helpful, and how. Of course, such a discussion is always held with the clients, so they can weigh in on the potential use cases of the AI tools.

For instance, the most common AI use case in software development lies in simplifying and automating repetitive tasks and seeking solutions to common issues. Meaning, developers don’t have to spend their time on simple matters easily replaced by ChatGPT or similar. In the given circumstances, no need for expertise or creativity is required.

Thus, such cases aim at increasing time efficiency and productivity. Meanwhile, the team can focus on more complex and heavy processes, reducing the development time and focusing on creative and innovative aspects of the job.

In other cases, AI can help develop early templates or start codes, run reviews, debugging, and testing processes. Other examples include:

  • generating code snippets,
  • additional code reviews,
  • identifying and suggesting code refactoring opportunities,
  • test case generation, and
  • data analysis.

At the same time, regardless of AI case use, developers must never compromise the confidentiality of the entrusted information or create security breaches. No passwords or sensitive data should be a part of the AI coding process.

Integrating AI into the development process: Best practices

By learning more about the possibilities of AI and opportunities for software development, our managers strive to create an AI integration system that will minimize the given risks and meet our clients’ demands.

Thus, the five pillars of the new system will be:

1. Training and education

All team members must be well-trained and aware of AI work and its limitations. We prioritize education and ensure that all developers involved with AI software development understand this tool, as well as other programming fundamentals. Thus, they can recognize and react to any issues on the spot.

2. Quality supervision

Each project must have a senior supervisor who will monitor the AI incorporation, evaluate the quality standards, and ensure efficient and productive work. Such monitoring will ensure the strong quality standards we uphold at Eastern Peak.

3. Testing

As always, testing remains a big part of any software development process. Our team will test AI-generated code searching for possible areas of improvement. No bugs or other issues will transfer to the proceeding stages.

4. Limitations

Our teams understand the limitations and weaknesses of such tools as ChatGPT and other similar ones. The development process must not trigger any AI risks. Thus, software engineers will only utilize AI tools in their strongest areas, relying on use cases from the above. So far, developers can’t solely rely on ChatGPT or similar tools in their work.

5. Communication

Transparency and open communication lie at the core of any cooperation. Our clients can always choose between using or not using AI technology on their projects and in other areas. If we proceed with the help of AI, they receive frequent updates on their project’s development.

These reports also include information on the code generated by ChatGPT or other AI tools. Such communication builds mutual understanding between all parties, keeps clients in the loop, and ensures their full knowledge of the steps taken at each stage of the process.

In addition to the five main working principles, all development processes with AI tools undergo tireless supervision by our project managers. They ensure developers understand all the AI tools’ risks and limitations, stick to the given guidelines, and maintain constant communication with the clients based on the utilization of AI in the coding processes.


Benefits of using AI in software development: Eastern Peak’s experience with AI thus far

Our experience with using AI in software development has so far shown significant benefits in a variety of areas. These are the three main improvements we are proud to share.

Increased efficiency

AI tools help us with automating tasks in software engineering, thus, increasing our experts’ efficiency and productivity. Automatization gives our developers more opportunities to focus their attention on more important tasks, leaving all the repetitive but time-consuming elements to the machine.

Sure, AI isn’t always the solution to completing projects in fewer hours. Though, it does positively impact the quality and efficiency of engineers’ work. Now, developers can dedicate more energy to complex tasks and creative work. Hence, they have more time for the most meaningful elements of the project.

Higher quality

AI can analyze codes and detect human errors. While our developers have to monitor ChatGPT or other AI tools, the models can do the same for our engineers. Thus, a language model can assess and detect errors and bugs missed by the human eye.

It can also offer improvements and reduce other risks down the line. As a result, we will introduce the final project having been edited by people and machines, leaving no chance for errors.

Enhanced accuracy

AI can easily work with large data. It can process large amounts of information within seconds and provide accurate and comprehensive feedback, determining any issues or odd patterns people may miss. Thus, having AI tools as the last proofreading resort, we may guarantee an extra layer of security and ensure higher accuracy in the final product.

These are the most apparent advantages we have discovered so far. However, we believe that this is only the beginning of AI’s role in software development. Currently, we are only testing and reviewing what benefits and abilities AI-powered software development can bring to the table. Since this technology is fresh and new, it is important to understand the risks and limitations, while respecting our clients’ needs and preferences.

Overall, it can be only a matter of time before the vast expansion of Artificial Intelligence in software engineering in the near future. In fact, soon developers’ teams will probably have special roles for people responsible for ChatGPT coding or other similar tools. So, by accepting the new sets of technology today, we are being prepared for the upcoming changes of the future.

The potential of AI in software development

The integration of artificial intelligence in software development was a long change coming. It is a glimpse into the IT industry’s future, where we may start relying on AI-enabled tools more and more frequently. Currently, this change is slowly transforming the industry, challenging traditional coding processes and introducing new solutions.

We, at Eastern Peak, understand the importance of moving with the time. Hence, we offer our services where we use the benefits, and benefits only, of AI in software development projects.

If you are interested to see the usage of AI in software development for your project, contact our team for more details. Book a free consultation and witness the future of software engineering with AI technology firsthand.

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