The development of Generative AI in recent years has brought back to the forefront discussions about how AI can be used in software development. Product teams in both startups and enterprise companies are lured by the idea of accelerating development with the help of AI. But what are real AI capabilities in code creation?
In this article, we will share the MobiDev experience and overview the possibilities and pitfalls of using artificial intelligence for software engineering, case studies where its application can bring benefits, and the best AI development tools available.
Software Development with AI: What It is and How It Works
Conversations about AI in custom software development often revolve around advanced tools like Copilot, Cursor, Zed, or similar plugins. These tools have gained popularity for their ability to improve productivity, but it’s important to understand their underlying mechanics.
At their core, these plugins analyze your existing project code and directly pass it, along with the specific prompt, to an external large language model (LLM). Inside the LLM, the code and prompt are transformed into vectors and manipulated to generate responses tailored to your query. Here’s how it works:
- Code context collection – the plugin scans your project to gather the necessary context.
- Prompt formation – based on the context, the tool creates a prompt that outlines the information or assistance you need.
- Querying the LLM – this prompt is sent to an external LLM, which processes the input and provides a response.
- Output generation – the LLM’s response is tailored based on the project’s code or, if the project is new, it delivers a general response shaped by the data on which the model was trained.
This flow has a key limitation: if the context provided by your project is limited or non-existent, the AI’s output will be broad and dependent entirely on the training data of the model being used.
An O1-specific model can be a viable alternative to other models that different tools like Copilot apply. This model integrates additional components such as contextual understanding and chain-of-thought reasoning, which improve their ability to solve complex problems.
The O1-specific model is capable of in-depth analysis of the project’s code or relevant context. It includes an advanced reasoning step where the model simulates multiple logical possibilities to arrive at the best solution.
With this approach, O1-specific models can tackle intricate challenges that standard tools might struggle with; and its output seems to be more logical and complete.
Top 5 Use Cases of How to Use AI for Software Development
The integration of AI into software development has unlocked immense potential for businesses that launch new or improve existing products. Here are the top five ways AI is transforming software development and the impact these innovations have on businesses:
1. Streamlining code reviews and debugging
AI-powered tools can automatically scan codebases to identify potential errors, bugs, or inefficiencies. By analyzing patterns and historical bug data, these tools provide instant feedback, allowing developers to address issues early in the development lifecycle.
Business impact:
- Reduces downtime caused by bugs
- Improves software quality, leading to better customer satisfaction
- Saves costs on manual code reviews and extensive debugging efforts
2. Accelerating prototyping and development cycles
AI enables automated code generation and assists in creating initial prototypes rapidly. By providing context-based suggestions, it allows teams to iterate and refine designs at an accelerated pace.
Business impact:
- Speeds up time-to-market for new software products
- Encourages innovation by allowing teams to experiment with multiple ideas
- Minimizes resource allocation during early development phases
3. Enhancing predictive project management
AI can analyze historical project data to predict timelines, allocate resources, and identify potential risks. With real-time adjustments based on evolving circumstances, AI ensures projects stay on track.
Business impact:
- Improves resource utilization, reducing waste and inefficiencies
- Increases project success rates by proactively identifying and mitigating risks
- Aligns project milestones with business objectives for optimal outcomes
4. Automating software testing
AI-driven automated testing tools can execute thousands of test cases simultaneously, identify performance bottlenecks, and simulate real-world scenarios. These tools adapt to evolving codebases, ensuring comprehensive test coverage.
Business impact:
- Enhances product reliability, minimizing post-release issues
- Reduces the time and cost associated with manual testing processes
- Improves scalability by accommodating larger and more complex projects
5. Optimizing team collaboration and documentation
AI systems can generate and update documentation automatically, extracting relevant insights from codebases and team discussions. This ensures that technical documentation is always current and accessible to team members.
Business impact:
- Reduces the burden of manual documentation, freeing up developer time for critical tasks
- Facilitates knowledge sharing and onboarding of new team members
- Ensures clear communication across departments, enhancing overall productivity
Adopting AI not only enhances the software development process but also allows businesses to remain competitive in the market. Statista reveals that 82% of developers use AI tools to write code, though implementation of such tools still has its limitations.
Challenges and Limitations of AI in Software Engineering in 2025
The quality and functionality of AI in software development can vary depending on several factors. Understanding these differences is crucial for businesses seeking to optimize their software development workflows using AI.
- Diverse models across services.
Not all copilots or IDEs rely on the same underlying AI models. Each service uses a specific model, which influences its performance, capabilities, and output quality. - Variations in training data and context window.
The effectiveness of an AI tool depends on the data it was trained on and the size of its context window (how much data it can process in a single interaction). These factors impact the tool’s ability to understand and generate accurate results based on your input. - Models are static during use.
AI models don’t train themselves while in use. However, some services collect interaction data (if permitted) for future improvements. Businesses must be aware of data-sharing policies to ensure compliance with privacy standards. - Local options and flexibility.
While many services are available as plugins or integrated into IDEs, businesses also have the option to use local AI models. Local setups can offer greater control and privacy while avoiding reliance on external services. - Privacy modes and free subscriptions.
Some services offer a private mode to prevent data sharing, while others don’t. Free subscriptions may seem appealing but often come with hidden costs, such as limited features, data sharing, or restrictions that could compromise security or performance. - Adjusting the context for better results.
To be accurate, AI outputs may require clear input. Precise and well-structured information helps the AI model provide more relevant and accurate responses.
There are two main challenges when using AI in software development: code creation and code sharing.
In terms of code creation, limitations often arise from the architecture of large language models (LLMs), which make it difficult to generate code that qualifies as commercial property. While OpenAI offers assurances and Microsoft Copilot stands out as a robust choice, these tools primarily function as advanced autocomplete systems.
The issue of code sharing is even more problematic. Transferring code generated by AI to external companies typically requires explicit client approval to ensure compliance. For example, GitHub Copilot, linked directly to your GitHub repository, offers a subscription-based model that simplifies usage within its ecosystem. However, for businesses prioritizing privacy, local AI models provide a viable alternative, as they eliminate the need for external data sharing.
TOP 6 AI Tools for Software Developers
In the table below, we include 6 popular AI development tools along with their features and business impact.
| # | Tool | Description |
|---|---|---|
| 1 | GitHub Copilot | An AI-driven coding assistant that integrates seamlessly into your development workflow, offering context-aware code suggestions as you type. It learns from the patterns in your project and provides intelligent completions. Developers mention that it may be the right tool to use with TypeScript, JavaScript, Dart, and Python. However, Copilot can make mistakes when guessing function names. There are instances where it creates code with subtle bugs which forces developers to spend extra time analyzing the output. Overall, Copilot is pretty good at generating simple repetitive patterns and auto-completing documentation. |
| 2 | GPT-Engineer | An AI-powered tool that aims to simplify and speed up app development. It can outline a solid architecture, select appropriate frameworks, and generate organized code. Upgrading to GPT-4 can slightly improve the output, offering cleaner code and better implementation. Overall, GPT-Engineer holds potential, but it's not yet reliable for handling complex coding projects without significant manual intervention. |
| 3 | Cursor | An AI-driven code editor built on Visual Studio Code, designed to optimize developer productivity with intelligent code suggestions and natural language editing. It features advanced AI pair programming, multi-language support, and SOC 2-certified privacy measures. Cursor also includes a conversational AI assistant for real-time problem-solving and refactoring. |
| 4 | Aider | A command-line-based AI assistant tailored for pair programming. It interacts directly with large language models like GPT-4 to assist with code writing, debugging, and multi-file editing. With automatic Git integration and customizable prompts, Aider may look appealing to developers who prefer terminal-centric workflows. |
| 5 | Replit Ghostwriter | An AI tool built into the Replit platform, focusing on collaborative and real-time coding environments. It helps users by generating code snippets, debugging errors, and even suggesting improvements for existing code. It’s useful for beginners or small teams looking for an all-in-one coding and AI solution. |
| 6 | Cody AI | A versatile coding assistant that can be used with VSCode to improve the workflow. It excels at summarizing code blocks, making code reviews and revisiting old projects much more efficient. Cody AI fills in the gaps in log statements, error messages, and comments, saving time on repetitive tasks. Also, its capability to auto-generate tests is surprisingly robust. However, when working with markdown, its suggestions are often verbose and overly positive, and getting concise or critical feedback can be challenging. Additionally, its inline suggestions sometimes feel out of sync with the actual context of the code. |
To write the code itself, it’s possible to use local AI models with any IDE or plugins for enhanced control and privacy. ChatGPT is another option, either in subscription mode (though it’s recommended to avoid sharing sensitive information) or in free mode, without sharing documentation or full source code.
The alternative option is integrating GitHub Copilot with a GitHub repository in subscription mode. However, for any external solution, it’s essential to obtain explicit approval from the customer. In all cases, developers must avoid sharing credentials or sensitive information to meet security and compliance requirements.
So, Can AI Replace Software Engineers?
No, AI cannot replace software engineers. While it is a powerful tool for automating tasks and streamlining development processes, human expertise is crucial for creative problem-solving, strategic planning, and crafting high-quality, context-specific solutions. AI serves only as an assistant, not a replacement.
Streamline Your Software Development Process with MobiDev
MobiDev has been in AI app development for over 6 years and in software product development services for over 15 years. Our team realistically understands for what tasks it’s best to use AI and when you need to involve experienced engineers.
If you’re looking for options to streamline your development process, the best option can be to strengthen your development capabilities with external expertise. MobiDev offers team augmentation and dedicated development team services to meet your needs at any stage of your product development.
Feel free to contact us to discuss your needs and tap into MobiDev’s expertise!
