Is it possible to save more than 20 working hours a month, and how can we optimize our work to manage all tasks successfully? A
survey conducted by Telerik Academy among 150 business representatives in Bulgaria indicates that people who use artificial intelligence in their daily work save an average of 4.39 hours per week. At the same time, over 50% of respondents express a lack of confidence in how to fully utilize AI assistants.
Regardless, AI tools already play an important role in business and provide a competitive advantage to anyone who understands them. The path to this involves targeted practical training that brings effective and innovative solutions to businesses.
The need for this is recognized by the teams at Telerik Academy and the Bulgarian tech company SiteGround, who are planning a two-week practical training program, GenAI for Developers, on the use of large language models (LLMs) in software engineering. The lead lecturer in the program is Stephen Tsvetkov, Learning Experience Design Manager at Telerik Academy. He is joined by Daniel Kanchev, Director of Product Management; Yasen Kiprov, AI Team Lead; and Krasimir Chariyski, Front-end Team Lead at SiteGround, who are responsible for all internal AI initiatives and AI integrations in SiteGround’s products.
They will share how they obtain resources for implementing internal AI solutions, which can be directed both toward clients and employees. After more than a year of focused efforts, they have several solutions in production, one of which is used by their engineers to accelerate front-end development.
See what they have to say about the training and
enroll to benefit from their experience and useful advice, following their path to success.
Tell us about the business case you will present to the participants in Telerik Academy's Sprint?
Yasen: During SiteGround's session, participants will learn about specific tasks our Front-end team managed to solve using generative AI. For the demonstration, we chose the company’s front-end style guide—a long-maintained project with a large amount of code that must meet high-quality standards.
What challenges did the team face during its implementation in the company?
Krasi: There were several key challenges. The main one was the lack of training—we started using it, and the training came later, by which time we already had a rough idea of what we were doing.
Secondly, we had security concerns—until we switched to an internally developed version, it was prohibited to copy and paste company code into public spaces. Once the internal GPT was released, this limitation was lifted, leading to better responses.
Lastly, we faced issues with GPT hallucinations—until people learned how to write better prompts, there were many instances where ChatGPT provided answers and made things up.
How did its implementation help the company?
Dani: Implementing Generative AI at SiteGround significantly impacted our developers by providing tools for automation and innovation. With Generative AI, we accelerated the development process by automating routine tasks (such as refactoring) and generating code (for tests) that meets our high-quality standards. This not only increased our teams' productivity but also allowed them to focus on more complex and creative aspects of projects. Additionally, AI helped us identify and fix errors more quickly, leading to more stable and reliable products. We also value the impact on junior colleagues, who shorten their training periods with AI assistance.
What will participants take away from the Sprint training?
Yasen: After covering the basics of programming with an AI assistant, the example from SiteGround will give participants a better perspective on how assistants help in working on real projects.
Krasi: Sprint training participants will have the opportunity to explore two real-life examples of solving complex tasks. The first example will involve a large context for a prompt, helping them understand how to structure and formulate their queries more effectively. The second will focus on problem-solving through iteration, demonstrating how to develop and refine their solutions step by step. These practical examples will provide participants with valuable skills and knowledge they can apply to their own projects and tasks. They will learn how to approach complex problems confidently and use various techniques to optimize their solutions.
Dani: Participants in the Sprint training will gain valuable insights into the strategic importance of Generative AI in software development. They will understand how AI can be integrated at different stages of development to improve efficiency and code quality. They will learn how to use AI tools for automating routine tasks, generating code, and identifying errors. Moreover, they will receive inspiration and motivation to view Generative AI not only as a tool for solving specific tasks but also as a key element for long-term growth and innovation in their projects.
