Do these 3 things before closing this tab:
1Scan for outdated or missing drivers - takes under a minute2Clear out junk files and repair common Windows errors3Fix the driver behind crashes, sound loss and screen glitchesUse both: an IDE provides the editing, navigation, debugging, and project tools you control directly, while an AI coding assistant can offer local completions, explanations, or help with a multi-step task. Choose the least autonomous option that fits the work: make precise changes yourself, use completion for a small suggestion, ask chat for guidance, and delegate broader edits to an agent only when you can review its changes and commands.
What is the difference between an IDE and an AI coding assistant?
A traditional IDE (integrated development environment) is where you edit and navigate code, inspect project structure, and use development tools such as debugging features. An AI coding assistant adds generated suggestions, conversational help, or—in some products—an agent that can work through a task. They are not mutually exclusive: AI features can run inside an IDE, and their availability depends on the editor, extension, and version.
For example, GitHub’s feature matrix lists completion and agent mode across multiple IDEs, but does not imply that every editor supports the same features. Check the GitHub Copilot feature matrix for the exact environment you use.
Which approach should you use for each task?
| Task or need | Best starting point | Why |
|---|---|---|
| Make an exact, sensitive change or inspect behavior closely | IDE tools and manual editing | You retain direct control and can follow the code and its behavior step by step. |
| Complete a small, local piece of code | Inline AI completion | Review, edit, accept, or reject a suggestion immediately. |
| Understand unfamiliar code or discuss an approach | Chat or Ask mode | You can get an explanation or bounded suggestion while keeping edits in your hands. |
| Handle a clearly described task spanning several steps or files | Agent mode, selectively | An agent may inspect the project, propose edits, and iterate, but its changes and commands need review. |
Keep manual control for precision and learning
Use ordinary editor and IDE capabilities when learning a codebase, making a narrowly scoped change, or tracing behavior where you need to see exactly what happens. AI can still help explain a passage, but you do not need to delegate the edit.
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Use completion for a small suggestion
Inline completion works best as a short feedback loop: inspect each suggestion and accept, revise, or reject it. Treat generated code like a proposed change, not an instruction to stop reading.
Use chat for explanation or bounded advice
GitHub distinguishes Ask, Agent, and Plan modes in its IDE chat documentation. Ask is intended for questions and suggestions; its documentation says it can help you understand code without modifying the project. Use it to clarify unfamiliar logic or consider an approach when you want to make the changes yourself. See GitHub’s IDE chat documentation.
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Use an agent only when you can supervise the work
GitHub describes agent mode this way: “Use agent mode when you have a specific task in mind and want to enable Copilot to autonomously edit your code.” Its IDE agent can determine which files to change, offer edits and terminal commands, and iterate on issues. You can review streamed edits and accept or reject proposed commands, unless command execution is configured to run automatically. That makes an agent more suitable for a clear multi-step task than for an open-ended request whose result is difficult to verify. Review the agent-mode guidance and your command-execution settings before delegating.
What should you check before relying on an AI feature?
- IDE and version: Confirm the feature exists in your editor and extension release; support is not identical across IDEs.
- Scope: Decide whether you want an explanation, a suggestion, or edits across files. Avoid granting an agent broader work than you can inspect.
- Review and recovery: Inspect proposed changes before accepting them, and understand how your project tracks or reverts edits. Check commands individually if they are proposed for execution.
- Usage terms: GitHub says each agent prompt consumes GitHub AI Credits. Check current plan terms and allowances before making an agent part of a routine workflow.
Do AI coding assistants improve productivity?
The available evidence does not establish a universal productivity winner between AI assistance and traditional IDE workflows. A JetBrains Research article dated April 15, 2026, describes a mixed-method study of two years of telemetry from 800 developers: 400 observed AI Assistant users and 400 non-users. It reports 151,904,543 logged events and a survey of 62 developers, alongside interviews. The researchers tracked activity such as typed characters, debugging starts, delete/undo activity, external paste events, and IDE activations; they say the goal was to detect workflow patterns, not establish causal effects. See JetBrains Research’s study account.
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In that study, the AI-user group increased typed characters by almost 600 per month, compared with an average increase of 75 characters per month among non-users. Those figures describe typing behavior. They do not show that AI caused the difference or that developers delivered faster, produced better-quality code, or became more productive.
A separate JetBrains Research paper, identified as January 2026 and published for ICSE-SEIP ’26, interviewed 35 professional developers across adopter, churner, and non-user groups and included an internal prediction market of 102 practitioners assessing 12 desired features. It reports alignment around implementation and context-awareness features, while proactivity and maintenance were underestimated and technically unaddressed in its analysis. This is evidence about developer needs and perceived feasibility, not a head-to-head product test. Read the January 2026 paper.
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These studies come from JetBrains Research, a vendor-affiliated research organization. Their findings are useful for understanding observed behavior and reported needs, but should not be treated as neutral proof that one workflow is best for every developer.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How common is AI coding assistance?
In its 2026 Developer Ecosystem Survey, JetBrains reports that 90% of professional developers used AI coding agents at work at least weekly and 68% daily. The survey received responses from more than 15,000 professional developers worldwide and was statistically reweighted; its published period is May–July 2026. These are survey adoption figures, not a controlled assessment of tool effectiveness or a recommendation for an individual task. See JetBrains’ 2026 Developer Ecosystem Survey.
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The same survey article reports changes in self-reported workplace use: Claude Code was around 39% in May–July 2026, up from 18% in January 2026; GitHub Copilot was 21% in May–July 2026, compared with 29% a year earlier; and Cursor was 12% in May–July 2026, compared with 18% in January 2026. It also reports that 9% used JetBrains AI in IDEs and/or Junie at work. These figures describe reported adoption in that survey, not comparative code quality or suitability for your work.
Quick Recap
A practical decision rule
- Need exact control or close inspection? Make the change with your IDE’s editing and debugging tools.
- Need a small local suggestion? Try inline completion and review it before accepting.
- Need to understand code or explore an approach? Ask chat for an explanation or bounded advice without handing over the edits.
- Need a clear task done across multiple steps or files? Consider agent mode if you can inspect the edits and proposed commands, and have verified how command execution is configured.
- Before adopting any feature: Verify IDE support and current usage terms for your exact setup.
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