JetBrains AI Assistant: how to review completions when the IDE looks like it already approved them

Published 2026-04-26 · 5 min read

JetBrains AI Assistant adds AI-powered completions, an AI Chat panel, and inline AI Actions to IntelliJ IDEA, PyCharm, WebStorm, GoLand, and the rest of the JetBrains IDE family. Inline completions appear as gray ghost text as you type — accept with Tab, just like GitHub Copilot. The AI Chat panel generates larger blocks of code you apply with a diff button. AI Actions (right-click → AI Actions) transform selected code in place.

The technical integration is excellent. The attention problem is different from other tools and comes directly from that excellence: JetBrains IDEs already show you a rich layer of code quality signals — inspection warnings, type-error highlights, parameter hints, smart completions based on your project’s actual types. When AI-generated code appears in that same environment with no red underlines, the IDE’s credibility bleeds into the AI output. “No warnings” starts to feel like “reviewed.”

Why JetBrains AI’s attention problem is specific to the IDE

Every other tool in this series has an attention problem rooted in the AI experience itself: Copilot’s ghost text arrives before you finish thinking. Cline’s approval cadence creates fatigue. Aider’s y/n bulk approval introduces bias. In each case, the AI experience itself is the source of the friction.

JetBrains AI’s attention problem is borrowed from the IDE. You have spent years developing trust in IntelliJ’s signals. The inspection system catches real bugs. The type checker finds real errors. When those signals are absent, you relax. AI completions that pass type checking inherit that relaxation, even though the type checker cannot evaluate whether the logic is correct, whether the algorithm is appropriate, whether the error handling is complete, or whether the function is doing the right thing in the right context.

This is trust laundering: the AI’s output passes through a trusted system and comes out with an unearned credibility stamp.

The three JetBrains AI attention traps

1. The inspection pass as implicit approval

IntelliJ’s static analysis is fast enough that by the time you look up from the keyboard after accepting a completion, the inspection has already run. If there are no yellow or red highlights, the natural interpretation is “the IDE checked it.” But inspections verify structure and types, not semantics. A method that calculates the wrong result, calls the wrong API endpoint, mutates state it shouldn’t touch, or silently swallows exceptions will produce zero inspection warnings as long as the types are consistent.

The inspection pass creates a cognitive signal that should trigger review but doesn’t, because it looks like review already happened. The absence of warnings is not a code review. It is a type check. Those are different things.

2. The AI Chat diff panel looks like a PR review

When you ask AI Chat to generate or refactor a larger block — implement this method, add error handling, convert this loop to a stream — the result appears as an inline diff with green additions and red deletions, rendered directly in the editor. This looks exactly like a git diff or a pull request review. The green/red pattern has a decade of training behind it: you review these, you decide whether to accept, you do it deliberately.

The trap is that the familiar visual pattern substitutes for the actual review. Scrolling a green-highlighted diff and clicking Apply triggers the same “I reviewed this” feeling as approving a PR — even if you didn’t actually read what the green lines do. The visual ritual of review is not the same as review. The diff view is a display, not a guarantee that you engaged with the content.

3. AI Actions inherit refactoring trust

JetBrains’ built-in refactoring tools — Rename, Extract Method, Introduce Variable — are highly reliable. Years of use have built a strong association: IDE refactoring = safe, mechanical, predictable. AI Actions (Generate, Explain, Transform, Fix) live in the same right-click menu. The proximity is a UX design choice that creates a trust transfer problem: AI Actions are not mechanical transforms. They are generative outputs that can change logic, add or remove behavior, and introduce new dependencies, all in a single operation with no diff shown before application.

Because AI Actions feel like refactoring, the review cadence applied to them tends to match the refactoring cadence: fast, incidental, low scrutiny. That is the wrong cadence for a generative operation.

Three fixes

Read past the cursor before pressing Tab

JetBrains AI completions, like Copilot’s, appear as ghost text. The natural response is to read to the end of what you were typing, see the completion continue correctly, and press Tab. But the completion often continues beyond the logical end of what you intended — additional method calls, extra parameters, a different return path.

Before pressing Tab, read the full completion to its last character. If it introduces anything you did not intend — an extra call, a different variable, a condition you did not specify — reject it and type the next few characters manually. Three or four characters of manual typing usually changes the ghost text to something closer to what you actually wanted. This takes two or three seconds per completion and prevents the most common class of AI completion error: the completion that starts right and ends wrong.

Find the error path in every AI Chat diff before clicking Apply

When AI Chat generates a method or a block, before clicking Apply, find where it handles errors. Scroll the diff and look for the catch block, the null check, the empty-result path, the network failure case. If you can’t find it in thirty seconds, that is the most useful signal the diff can give you: the generated code does not handle that case. Decide before applying whether that is acceptable or whether you need to add it.

This is not a full review. It is one concrete binary check that takes thirty seconds and consistently surfaces the most common gap in AI-generated business logic: the happy path is correct and the error path is missing.

After AI Actions, run Inspect Code before continuing

AI Actions modify code in place with no diff view. After any AI Action application, run Code → Inspect Code on the changed file (or use the toolbar shortcut). The inspection run is not a substitute for reading — it catches structural and type-level issues the AI introduced, not logic errors. But it serves as a forcing function: the two-second wait while inspection runs is a natural moment to actually read what changed before moving on. Without that pause, the next keystroke follows the AI action immediately, and the window to catch the change closes.

Pair this with a quick read of the changed lines. Inspection + read takes thirty seconds for a small AI Action and eliminates the most common regression pattern: a change that type-checks but breaks behavior.

JetBrains AI is the deepest IDE integration in this series. The tooling is genuinely excellent and the completions leverage real project context that cloud-only tools can’t access. The review challenge is precisely that integration: every quality signal you have learned to trust in the IDE now wraps AI output that was never subject to those signals. Separating the IDE’s credibility from the AI’s output is a deliberate act. It does not happen automatically.

Reading full completions before Tab, finding the error path in every Chat diff, and running an inspection after AI Actions converts the IDE’s power from a review substitute into a review accelerator.

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