Ever since the coming of Artificial Intelligence, there has been quite a lot of buzz around the large language models (LLM), mainly because of their wide applications, especially in the field of software development. With breakthroughs in natural language processing (NLP), software professionals are able to tap into the potential of LLMs with tailored prompts – they exploit these models to effectively build software applications.
It is not an overstatement that LLMs, part of artificial intelligence solutions, are revolutionizing the practices followed in software development, many software professionals rely on deep learning and NLP, with this they derive the best solutions with minimal or zero error.
LLMs are AI models, they are basically trained on large data sets. LLMs primarily deal with text as input and output, they are specifically trained in natural language processing and deep learning, they are subset of machine learning (ML). This helps the AI model to understand and comprehend how humans communicate, these models, with precise prompts, assist software professionals in a multitude of ways, this range from developing apps to generating code.
Leverage LLMs for Software Development Lifecycle
With innovative artificial intelligence models, you are able to employ LLMs, they assist you in terms of end-to-end software development.
Requirement Process
LLMs are beneficial right from the beginning of the SDLC process, you are able to use the model to develop project ideation, be specific about the right requirements. The model’s training uses larger datasets, this helps you to accurately identify the project requirements, it also provides suggestions based on market trends, user feedback, and best practices followed in the industry.
In addition, LLMS also assists you in terms of verifying if the requirements are accurate, you can check the requirement documentation if it aligns with the actual scenario, free of errors, and so on. All you need to do is to precisely present the complete scenario to the LLM, ask it to define the project requirements.
Building Architecture
LLMs come as a great help in deciding and suggesting suitable architecture for the software, it is rather a complex task for human professionals, they often need to spend a large amount of time in brainstorming ideas. These models simplify the overall process, with the help of LLM, developers are able to come up with suitable architectural patterns that are the right fit for the software.
LLMs analyze from various perspectives, they then bring out solutions, this helps in streamlining the development process, it also enables developers to come up with solutions faster. LLMs have wider applications when it comes to developing architecture, use the models to pick the right set of tools and programming languages, this process is important for the complete development process.
Code Development
LLMs are seamlessly relied upon for generating code, this becomes easy for developers, they are able to generate code for any specific part or function of the application. You can use LLMs to automate the repeated coding tasks, this reduces the overall time taken to develop the code, and to identify any flaws or bottlenecks.
Many AI models are there, they are specifically trained on datasets with code repositories and technical forums, many of them are extensively trained with various code platforms and general web data.
These models help you to translate your prompt into code, you got to verify if it is free of error, the main advantage of this function is that it reduces unnecessary time, and it ensures the overall quality of software.
Quality Assurance
Large language models provide assistance to conduct quality assurance, the software team is able to leverage the models to provide input requirements, the models then generate comprehensive test cases. This approach benefits the software development services in various aspects, the models generate test reports, they significantly help in checking the software and assist in providing maintenance.
Documentation
Software documentation is an important yet mundane task, manually document each and every step takes time and effort. LLMs can be of great help, they effectively generate project documentation based on the source code and system specifications.
It’s easy for you to develop different forms of documentation using LLMs by simply providing it with input prompts, the models give API reference documentation, code documentation, and many more.
With LLMs, you are able to utilize the time and effort that would otherwise go in preparing documentation in other important tasks, it increases the productivity and improves faster time-to-market.
Which LLM best fits your needs
It is one crucial step in software development by leveraging LLM, you need to choose the model that best fits your needs. The LLM that you choose, whether it is for mobile app development or automation, can significantly impact the speed and efficiency of the overall process. You need to fix the type of model you need – commercial or open-source one – based on your objectives and expertise, choose the one that best fits for your organization.
For instance, in case of open source LLM, Open AI’s GPT models are some examples, you do not require an expert team to setup the model, your organization can utilize it instantly. However, it is not the case with open-source model because it allows you to customize the model in a way you want, in other words, it is flexible, but to do that, you need an expert AI/ML team.
Similar to the key differences between custom software development and SaaS based solutions, these two LLMs also differ in a fundamental way – the upfront cost of commercial LLM is lesser than open source but API fees and other costs may add up with commercial model, this does not incur in open-source models.
Also keep in mind the privacy of your data while choosing a language model, some commercial models may send your data to third-party servers for data processing, however, with open-source model, you have complete control over your data.
To wrap it up
Large language models have their huge applications in the software development lifecycle, LLMs are effectively relied upon from the planning phase to the deployment phase. While choosing the perfect LLM for your organization, you need to consider several things – budget, data security, time-to-market, all play a deciding role. One more aspect is that although LLMs can generate code, human intervention is needed in checking the potential issues, the AI model can be used to assist the human developers and testers to fasten the whole software development process.