10 Things Every Java Developer Must Know About AI Right Now

2026-06-04 · Source: Towards AI - Medium · Field: Technology & Digital — Software Development & Engineering, Artificial Intelligence & Machine Learning · Depth: Intermediate, quick

Summary

AI is rapidly transforming the Java development landscape, moving beyond a research topic to integrate directly into IDEs, CI/CD pipelines, production APIs, and daily libraries. Despite Java's nearly three-decade tenure as an enterprise software backbone, developers must now adapt to this shift. Key tools like GitHub Copilot, JetBrains AI Assistant, and Tabnine have matured into genuinely useful AI code assistants for Java, aiding in generating boilerplate, writing unit tests, and suggesting idiomatic implementations. Effective utilization of these assistants hinges on proper prompting, as vague queries yield generic code, while detailed descriptions produce more relevant output. This integration necessitates a new mindset and skill set for modern Java development.

Key takeaway

For Java developers aiming to remain current and efficient, integrating AI code assistants like GitHub Copilot or JetBrains AI Assistant into your workflow is now essential. These tools significantly streamline tasks such as boilerplate generation and unit test writing. To maximize their utility, prioritize developing strong prompting skills; clearly describe your domain, constraints, and expected behavior to receive precise, valuable code suggestions rather than generic outputs. Your proficiency with these AI tools will directly impact your productivity and code quality.

Key insights

AI is fundamentally changing Java development, requiring developers to adapt and utilize new tools effectively.

Principles

• AI code assistants are mature and genuinely useful.
• Effective AI tool use requires precise prompting.

In practice

• Use AI assistants for boilerplate generation.
• Employ AI for writing unit tests.
• Leverage AI for idiomatic code suggestions.

Topics

• Java Development
• AI Code Assistants
• GitHub Copilot
• JetBrains AI Assistant
• Prompt Engineering
• Software Engineering Productivity

Best for: Software Engineer, AI Engineer

Related on AIssential

• 5 Free AI Tools to Understand Code and Generate Documentation — AI tools significantly streamline code comprehension and documentation generation for developers.
• JavaScript Developers Will Be Playing With Fire In 2028 — Here is the Substantiation. — AI's rapid code generation capabilities challenge traditional JavaScript development roles, necessitating developer evolution.
• The 6 Best AI Coding Assistants — AI coding assistants now offer deep codebase understanding and agentic capabilities, significantly boosting developer productivity.
• Presentation: Choosing Your AI Copilot: Maximizing Developer Productivity — AI copilots offer significant developer productivity gains, especially when integrated into broader workflows.
• GitHub Copilot app: The agent-native desktop experience — GitHub Copilot app centralizes agentic development, offering integrated tools for managing, inspecting, and automating AI-driven workflows.
• Writing Code with Github Copilot and ChatGPT Together — III — AI code agents excel at routine tasks but require human oversight for novel or complex problems.

Open in AIssential →

Editorial summary, takeaway, and curation by AIssential. Original article published by Towards AI - Medium.