By Vinícius Cruvinel — Flutter Developer at ília
As a development assistant for technology professionals, GenIA has demonstrated great capability. In this text, we will discuss its role in this field, its challenges, and solutions.
First and foremost, to demonstrate the importance of AI nowadays, it is crucial to understand that it is not a novelty. The fact is that AI has existed for quite some time, but only in recent years have we gained enough processing power to achieve the capacity to utilize a different type of AI, the so-called Generative AI.
What is GenIA?
In essence, the difference between traditional AI and Generative AI is that traditional AI is more focused on specific approaches, while generative AI is characterized by generating original content from a compendium of existing information.
This information is pre-fed into the original knowledge base or by algorithms with engines capable of searching for information emerging in real-time.
When we bring these functionalities into the software development context, we have a powerful aggregator of information that provides developers with incredibly agile and accurate responses.
The Evolution of Development with GenIA
This generative AI has been rapidly evolving, and its great differentiator is the ability to understand context and create continuity by establishing a conversation with the developer.
However, for AI to maintain the context and continuity of the conversation, we must establish parameters to narrow the scope of the content to be addressed. This is a fundamental point for a more effective result.
For example, we can specify the programming language that AI should use to generate code and, within that language, which packages and libraries it should utilize.
This parameterization makes all the difference in the result of the requests made by the developer.
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Benefits of GenIA for Technology Professionals
For developers who previously consulted each other, regardless of seniority levels, or conducted extensive and time-consuming searches on search engines and code-oriented tools.
This AI dynamic brings to the developer an assistant with high availability for idea exchange, concept explanation, analysis, and code generation.
This does not mean that the developer will copy and paste code, as they still need extensive programming knowledge. However, with GenIA working together, it will avoid much repetitive code and less typing due to the AI’s code suggestion features.
This will result in increased efficiency, fewer errors, and higher productivity.
Use Cases of GenIA by Professionals at ília
In one of the projects we developed at Ília, we used various generative AI tools, some examples being GitHub CoPilot, which completes the code we are typing, CoPilot Chat, to establish a conversation, and additional standalone assistants like OpenAI’s GPT Chat and Google’s Bard, which has now been renamed to Gemini.
At the moment the code is suggested by AI, the developer’s critical thinking comes into play to determine if that code is repetitive or not.
At this moment, a decision-making process occurs to accept the suggestion or if the logic of that scope requires a new reasoning not yet aggregated by the suggestion engine and opting for building entirely new code.
Another point to highlight is that during the code construction process, something that was not being considered by the developer may end up being suggested by AI, and the presented solution ends up being the most suitable in that context.
To make it even clearer, a good example we can mention is the ease we have of applying regular expressions when we need to create code to apply a CPF, CNPJ, or ZIP code mask.
This avoids the need for the developer to search in tools that were commonly used before, such as Stack Overflow or the Google search engine.
This experience of using these tools in our projects has optimized deliveries, increased unit test coverage, and further solidified the developers’ knowledge.
We also use GenIA in our projects with Salesforce, read also: Understand now how Salesforce Einstein transforms projects with Artificial Intelligence
Challenges and Solutions
We can highlight that the main challenge for the developer is understanding how to parameterize AI to narrow down the scope of responses and achieve a more accurate result while maintaining context and conversational continuity.
This means that as the developer parameterizes AI by defining their tool, package, and pattern preferences they use in their daily lives, all code suggestions will use these parameters instead of other possibilities.
This brings greater objectivity to the code generation process, thus avoiding wasted interaction with the AI tool being used.
The Future of GenIA in Development
With the constant evolution of these tools, which are often already integrated into the development environment, the developer begins to have a digital assistant that will help in the task of building increasingly complex and feature-rich applications.
This practice has shown that it can increase the level of quality and also offer more accessibility that can be prioritized in a more optimized way.
In technology, we always face paradigm shifts, what was conventional and right, ceases to be, and the developer must constantly adapt to new dynamics.
In the present and even more so in the future, this professional will type less and do less repetitive coding, but will need to increase their technical and behavioral skills as they will automatically be elevated to a level of participatory leadership in the projects they will work on.
Personally, I view these changes positively and believe that in a more distant future, Generative AI will bring us many benefits, revolutionizing our world and how we interact with knowledge, further opening up possibilities in the job market.
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