We are entering the era of generative AI. The disruptive power of this technology is enormous and is already transforming the way we work and live. And one of its main capabilities lies in code development.
Indeed, this technology can automatically generate documentation and improvements on source code.
From completing a line of code to assembling readme files on projects. From correcting formatting and logical structures based on best practices to creating complete functions. Development teams that know how to capitalize on this power will be able to multiply their productivity to unprecedented levels.

Use case: Generative AI in Banking and Fintech
The benefits of GenAI for software development apply to all industries. However, in some areas in particular it manages to stand out significantly.
This is the case of financial services companies, both traditional and fintech. A segment characterized by a very high level of competition and that constantly needs to adapt faster and faster to the customers’ requirements and preferences.
At the same time, it must comply with strict regulations and show that all its products, especially digital ones, are strong, reliable and secure.
A development team using generative AI and applying known best practices can achieve all of these goals. On one hand, speed in the creation of products and services. On the other hand, more testing to ensure good performance and data privacy.
The dangers of generative AI in code development
As always when a new technology appears, especially if it is so disruptive, there are also some risks and challenges to consider.
This is, of course, the case for generative AI applied to code creation. For example, the risk of algorithms producing code that is buggy or vulnerable to cyber-attacks requires special attention.
In addition, over-reliance on generative AI in programming could lead to a decrease in the quality of human-produced code, which could affect the scalability and long-term maintainability of digital products.
Lack of complete understanding of the internal processes of generative AI, meanwhile, could result in the introduction of unwanted skews in the generated code. This could amplify existing disparities and perpetuate application differentiation.
Given the above, it should be clear that generative AI is a tool to improve the productivity of a development team. It in no way replaces human skills.
Can we rely on generative AI to create code?
In the context of the previously mentioned challenges, it is crucial to implement rigorous security and oversight measures to mitigate the risks associated with implementing generative AI in code development.
It is the developer’s experience and knowledge that sets boundaries and helps the results of generative AI to be reliable.
On the other hand, there is a global trend that seeks to regulate generative AI to reduce uncertainties and manage concerns.
Among other things that specifically affect software development, different alternatives are being analyzed to protect the copyright of the code created. Work is also being done to protect data privacy and ensure the ethical and responsible use of technology.
Conclusions
The potential of generative AI to exponentially improve the productivity of a software development team is undeniable.
At the same time, it is crucial to address the challenges and risks along the way.
This involves raising awareness and training development teams to understand the scope and the limitations of this technology. It is not a magic element that solves all problems, but an excellent assistant that boosts the capabilities of human beings who use it intelligently.
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