11 AI Tools That Speed Up IT Product Creation at All Stages of Development
Artificial intelligence is becoming an integral part of IT product development. AI not only speeds up processes, but also improves their quality and efficiency. We will consider 11 AI tools that are actively used in the software development industry.
According to researchthe global AI market in the IT industry will grow to $271.9 billion by 2028, showing a CAGR of 27.1%. This is due to the growing demand for business process automation, increased efficiency, and innovation.
AI is being implemented to optimize business processes and improve productivity. It can perform monotonous tasks faster and more accurately than humans. Analyzing large amounts of data and identifying patterns helps companies make more informed decisions.
Every year, the use of artificial intelligence in the IT industry becomes wider and deeper, penetrating all areas of business and everyday life. We have collected 11 tools that help at the main stages of product development:
Idea generation and conceptualization
These processes play a key role in product development and can be made more efficient with the involvement of artificial intelligence.
GPT models generate ideas and solutions, help brainstorm, and refine concepts. They handle large amounts of data and provide multiple perspectives when prompted correctly.
Example of use:
The company was working on a new project management service that required text content for the user interface and training materials. There was no UX writer on the team, and other specialists were overloaded with work, and the writing process dragged on.
Then the project analyst suggested using ChatGPT to automate this process. He created a prototype of ChatGPT integration with the internal content management system, which allowed generating texts based on the entered keywords and requirements.
The team began using the tool to quickly generate copy. For example, when designers were developing new screens for the service, they would enter a description of the functionality into ChatGPT, and the tool would generate suitable text for those screens (which only had to be checked). This allowed the team to reduce the time spent writing copy from several days to several hours.
The project was completed a month ahead of schedule and the product received high marks for its information content and ease of use.
This tool uses artificial intelligence to analyze product and feature ideas and instantly transform them into development specifications, use cases, diagrams, and user stories. Helps Agile teams create clear and concise user stories, acceptance criteria, and process diagrams. Facilitates knowledge sharing, adoption, productivity, quality control, and efficient implementation. Helps brainstorm and create technical documentation to quickly move into development.
Example of use:
The company is developing new functionality for a mobile app that helps travelers organize their trips. The customer set a task to add the function “Recommendations of local attractions” based on the user's location.
The analyst used Gluecharm to formulate a user story: “I am a traveler and want to see local attractions recommendations to explore new places during my trip.” The tool helped him structure the story, add attributes (e.g. recommendation priority, transport accessibility, difficulty level), and link this user story to others in the project.
Uses artificial intelligence to generate content ideas, help with SEO, and optimize content strategies. Helps create high-quality content that matches user intent. Its features include SERP research analysis, AI-powered content and briefing.
Example of use:
The author from the IT development team prepares posts for the company's blog about the use of new technologies in software development. An analyst helps in creating quality content for the target audience and visibility in search results. Using Frase, he examines pages with search results and content analysis to determine the most popular queries and topics in this niche. And with the help of the tool, he creates a brief for the author, including keywords, content structure and optimization recommendations for better ranking in search engines.
Design and prototyping
AI-powered design software can help create more efficient products. This process involves using generative design algorithms that suggest optimal design solutions based on specific criteria, such as material, cost, and performance requirements. When creating prototypes, AI can simulate how a product will work, allowing designers to make adjustments before a physical model is developed.
A tool that helps teams in the early stages of product development when stakeholders brainstorm ideas. It improves brainstorming sessions and fills in gaps with AI. Its capabilities include creating notes to summarize discussions, converting text to images, mapping user stories with user personas, creating sequence diagrams to review the main idea, generating code blocks with natural language processing capabilities.
Example of use:
Let's imagine that the company “Romashka” creates a product for a client – an advanced content management system (CMS) for large corporations. The team includes an analyst, a product manager and 2 developers.
At the initial stage of development, during the collection of ideas and requirements, the product manager organizes a meeting in Miro, creating a board with different sections for functionality, design, technical constraints and customer requirements.
During the meeting and brainstorming, all team members use interactive markers and sticky notes to present their ideas. The analyst uses the Miro Assist feature to quickly format and structure information to organize ideas and make them more understandable.
When discussing integration with other systems, the tool offers a list of popular APIs and tools that can be used. The team also uses interactive tools for voting and rating ideas, visualizing the results of the survey.
After the meeting, a summary and action plan is automatically created based on the ideas and solutions discussed and sent to all participants.
Thus, the AI tool helped improve communication and team efficiency in the early stages of IT product development.
Development
AI code generation is powered by ML algorithms trained using existing source code, often obtained from open-source projects.
There are three main methods of AI code generation:
Autofill function. Developers initiate code writing, and the AI tool attempts to automatically complete the code based on patterns learned from a training data set.
Natural language input. Developers formulate intents through natural language input, prompting the AI tool to generate code suggestions that match their goals.
Direct interaction. Developers communicate directly with the AI using a chat interface, sending specific requests or commands to fix errors, demonstrating the technology’s conversational capabilities. The user enters text prompts describing the desired code functionality. Generative AI tools respond to them by suggesting code snippets or generating full functions.
Let's look at the tools:
Developed by GitHub in collaboration with OpenAI, an AI-powered code completion tool that can easily integrate into popular integrated development environments (IDEs) like Visual Studio Code, offering context-sensitive code suggestions and completions as you type.
It uses OpenAI Codex, a language model trained on various code repositories, to generate code suggestions as developers type. OpenAI Codex is most powerful in Python, but it also supports other languages, including JavaScript, Go, Perl, PHP, Ruby, and TypeScript.
Example of use:
Let’s imagine that XYZ, a software development company, has decided to use GitHub Copilot to increase the productivity of its team and improve the quality of its code. Here’s how the team can use Copilot in their daily work:
Writing the main code. The developer begins typing code, defining basic functions. As you type, GitHub Copilot suggests function templates that you can quickly accept by pressing Tab.
Code optimization. Once the core functions are written, the developer decides to optimize them by adding data validation. When the developer starts typing the code for validation, Copilot offers standard validation methods that can be easily integrated into the existing code.
Testing and debugging. After writing the code, the developer runs the tests, and if errors are found, Copilot helps quickly fix them by suggesting possible solutions based on the context of the error.
Refactoring. Once the code is working, the developer decides to improve its structure. Copilot offers various approaches to refactoring, such as using classes to manage users, which makes it easier to further extend the functionality.
Code Review. Before merging changes into the main branch, another developer reviews the code. Copilot helps in this process by offering questions and suggestions for improving the code, which improves its quality and speeds up the review process.
Models OpenAI GPT
These, as we mentioned in the section on ideation and conceptualization, can be fine-tuned for code generation tasks as well. While ChatGPT isn’t specifically designed for code generation, it’s still possible. Developers can interact with models using natural language prompts to retrieve code snippets. Unlike GitHub Copilot, ChatGPT isn’t integrated with an IDE and has its own interface, allowing it to work with a variety of popular programming languages, including Python, JavaScript, C++, Java, Ruby, C#, PHP, and Go. You can even experiment with Dart, R, or Lua if you’d like.
Google Tools
Google has several tools for generating AI code, each with its own strengths and focus.
Trained to work with a large data set, allowing it to generate images, text, and code. Supports C++, Go, Java, JavaScript, Python, and TypeScript.
Uses the Pathways Language Model 2 (PaLM 2) to generate text and code in response to dialog prompts.
The second pilot project is an AI project based on Google models that runs in IDEs (such as VS Code or PyCharm), offering real-time coding assistance similar to GitHub Copilot.
Code Llama from Meta
An open-source AI model based on Llama 2, designed for code generation and discussion. It handles coding tasks among publicly available LLM programs. The model is designed to optimize developer workflows, facilitate their training, and improve software reliability and documentation. Supports Python, C++, Java, PHP, Typescript (Javascript), C# and others.
An AI-powered auto-completion tool that integrates with various code editors (IDEs) such as VS Code, IntelliJ, and Eclipse. The tool uses LLM, which processes sequential data and provides answers based on knowledge gained from training data. Supports JavaScript, Java, Python, TypeScript, PHP, and C++.
What's the catch?
Implementing AI code generation comes with some challenges. Studypublished in IEEE Transactions on Software Engineering, shows that ChatGPT doesn’t always get the job done—and, what’s more, doesn’t always produce a quality solution: success rates range from 89% for the simplest prompts to 0.66% for the longest prompts. These findings raise serious concerns about the reliability and quality of the code generated by ChatGPT, highlighting the potential risks associated with its widespread use.
As with any emerging technology, there are questions:
Reliability. Can you trust code created by AI? Research has shown that while AI assistants are generally reliable, they can sometimes create buggy or unsafe code, highlighting the need for careful code review. Studywhich studied the accuracy of code generated by Copilot, found that the quality of code generation by this product gradually declines after release – in addition, the tool often produces results that contradict the basic principles of programming in the chosen language.
Support. AI can create excessively complex code, use uncommon approaches and functions – and work differently for each developer. It will not be possible to synchronize the execution of tasks according to some pattern, and refraction of code from AI can also be very difficult. So this is not the best solution for collaboration.
Endless refinements. AI is trained on syntactically correct elements. So, although the generated code is almost always standard-compliant, it does not necessarily guarantee optimal performance or maintainability.
Loss of control. Some developers worry that relying too heavily on AI assistants could reduce their programming skills and experience. However, the key is to view AI as a valuable tool, not a replacement for human judgment and critical thinking.
Conclusion
Integrating AI into the creation of IT products is a significant step in the prototyping and development phase. AI tools have the ability to understand consumer needs, market trends, and the ever-evolving technological landscape and adapt to it. However, at the moment, the role of AI is not to replace human ingenuity, but to expand it.
Thank you for your attention!
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