JetBrains is integrating OpenAI’s GPT-5 into its developer tools to improve how teams design, reason, and build software inside their IDE. Across experiences like JetBrains AI Assistant and the Junie coding agent, GPT-5 supports long-horizon tasks—drafting, refactoring, debugging, and iterating with tests—while keeping developers in flow.

Coding productivity isn’t only about writing code faster. It’s about reducing the “hidden work”: chasing context, translating requirements, updating tests, fixing build failures, and rewriting the same explanations across tickets, docs and pull requests.

JetBrains is addressing that friction by integrating OpenAI’s GPT-5 across its tools and workflows—bringing advanced reasoning and generation directly into the environments developers already use.

Why this matters (beyond autocomplete)

Most teams already have code completion. The bigger unlock is long-horizon development work: multi-step tasks where developers must plan, run tools, interpret results, and iterate.

This is where GPT-5 helps: it can keep track of intent across a sequence of steps and produce higher-quality first drafts—so engineers spend more time on architecture and judgement, and less time on repetitive work.

What’s new: GPT-5 inside JetBrains workflows

JetBrains’ GPT-5 integration shows up in three practical places:

1) JetBrains AI Assistant (in-IDE chat + help)

Developers can use GPT-5 for explaining code, proposing refactors, drafting documentation, writing tests, generating examples, and translating requirements into implementation steps—without leaving the IDE.

2) Junie (agentic coding)

Junie is JetBrains’ coding agent designed for more autonomous, multi-step tasks. With GPT-5, it can handle more complex work: larger refactors, multi-file changes, and repeated cycles of “change → test → fix”.

3) Kineto (no-code app creation)

For teams building internal tools, GPT-5 can accelerate prototyping and app creation by turning prompts into usable single-purpose apps—helpful for internal workflows where speed matters.

How it works

The value comes from embedding GPT-5 where the work happens:

1. Context is nearby: the IDE contains project structure, code, errors, and tests.

2. Tasks are iterative: developers need a loop (edit → run → evaluate → adjust).

3. Outputs must be reviewable: AI-generated work still needs code review and governance.

When GPT-5 sits inside that loop, it reduces switching costs and increases throughput.

Practical examples for engineering teams

Example 1: PR-ready refactors
Ask the assistant to propose a refactor strategy, implement changes across multiple files, update tests, and draft a pull request summary. You stay in control, reviewing diffs and adjusting direction.

Example 2: Debugging and test repair
When a test fails, GPT-5 can help interpret stack traces, suggest likely causes, propose fixes, and generate additional tests to prevent regressions.

Example 3: Documentation and knowledge transfer
Turn complex modules into concise docs, onboarding notes, or “how this works” explanations—reducing tribal knowledge risk.

What teams should do first (a safe adoption plan)

JetBrains’ GPT-5 integration is most valuable when you treat it as a workflow capability, not a novelty.

1. Start with one workflow (test writing, bug triage, refactors, documentation).

2. Define quality gates (linting, test pass, review requirements).

3. Set access boundaries (repos, secrets, production data).

4. Pilot with a small cohort and measure: time-to-merge, review load, defect rates.

5. Scale with training (prompt patterns, review checklists, safe-use guidance).

Summary & next steps

JetBrains’ GPT-5 integration points to a clear shift: from “AI helps write code” to AI helps ship software—supporting long-horizon work inside the tools developers already trust.

Next step: If you want help designing an AI-assisted engineering workflow (governance, evaluation, rollout), Generation Digital can support your plan.

FAQs

What is JetBrains integrating into its coding tools?

JetBrains is integrating OpenAI’s GPT-5 into developer experiences such as AI Assistant, the Junie coding agent, and other AI-powered workflows.

How does GPT-5 integration benefit developers?

It helps developers reason through multi-step work—debugging, refactoring, test creation, and documentation—while keeping context inside the IDE and reducing repetitive effort.

Who will benefit from this integration?

Teams using JetBrains IDEs—from individual developers to large engineering organisations—especially those looking to accelerate long-horizon tasks and improve first-pass quality.

How should teams use it safely?

Apply standard engineering controls: code review, test gates, secrets management, access limits, and auditability for AI-assisted changes.