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What code generation in an IDE can do now
Modern IDE assistants combine several kinds of help. They can suggest code while a developer types, respond to questions about code, use project information to inform an answer, and—in some products—edit multiple files. These capabilities overlap, but they differ in how much context they use and how much control they take.
Inline completion predicts code at the cursor
Inline completion displays proposed code, often as ghost text, while a developer is typing. GitHub documents Copilot suggestions for many languages and frameworks, as well as examples involving APIs, database queries, and infrastructure as code. The developer can accept, modify, or ignore a suggestion. This remains useful for routine implementation, but it generally leaves the developer responsible for deciding what to ask for and where the code belongs.
Chat helps explain and transform code
Conversational assistants can explain selected code, describe API calls, define unfamiliar domain terms, and provide usage examples. A Google Research study presented at ICSE 2024 found prompt-less interaction inside an IDE promising for such tasks. That finding concerns particular developer-assistance tasks; it does not establish that every chat interaction improves productivity or produces correct code.
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Repository-aware assistance uses more than the current line
When an assistant can consider project files and other development context, it may give a more relevant answer than a model working from a short prompt alone. Current VS Code guidance recommends providing useful context such as source-control changes, terminal output, and test failures. Context is not automatically complete or correct: developers still need to check whether the assistant has understood the relevant files, constraints, and current state.
Agents can make broader edits
Agentic tools can act on a task rather than only propose a line or answer a question. GitHub documents agents that edit files across a workspace; Cursor is a prominent AI-first editor in this category. A multi-file task might involve coordinating changes across related parts of a project, but the ability to make broad edits increases the potential impact of a mistaken assumption. The key distinction is the scope of action, not a guarantee of autonomy or quality.
How the developer workflow is changing
The emerging loop is: state the intent, give the assistant relevant context, inspect its proposed change, run project checks, and revise. The model can draft implementation choices, but the developer remains accountable for whether those choices fit the project.
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- Describe the outcome. Specify the behavior, constraints, and relevant edge cases—for example, what a feature should do and what existing behavior must remain unchanged.
- Supply the project context. Point the assistant to relevant files, repository instructions, diagnostics, documentation, or test failures. A vague request with missing context invites a plausible but mismatched implementation.
- Review the diff. Inspect every changed file for assumptions, interfaces, error handling, dependency choices, and side effects. Treat the proposed diff as a draft, not as an approved implementation.
- Run validation. Use the project’s tests, linters, type checks, static analysis, and security checks. Resolve failures and understand warnings rather than assuming generated code is correct because it looks valid.
- Revise and approve deliberately. Give the assistant specific feedback or make the correction yourself, then review the updated change before merging.
Why context and validation determine the result
A language model can produce code that looks convincing while being syntactically or semantically wrong, or while missing the developer’s intent. If it lacks project conventions, interface definitions, or the failure that needs fixing, it may fill in those gaps with assumptions. More context can help, but it is not a substitute for checking the result against the actual project.
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For teams, useful context can include repository-level instructions, the files involved in a change, relevant documentation, current diagnostics, and test output. The goal is not to feed the assistant everything; it is to provide the information that constrains the task and makes the result verifiable. A focused request paired with a concrete failing test is generally easier to assess than a broad request to “fix the project.”
Validation should match the risk of the change. A small, isolated edit may need focused tests and a diff review; a change to authentication, data handling, dependency management, or infrastructure warrants more extensive checks and human scrutiny. Passing tests are valuable evidence, but they only cover the behavior those tests exercise.
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Productivity evidence needs careful interpretation
GitHub reported that 85% of developers felt more confident in their code and 88% felt more in the flow while using Copilot. These are reported perceptions from prior research summarized by GitHub in an article updated in 2025; they are not direct measurements that code quality or delivery speed improved by those percentages.
A 2026 Carnegie Mellon University/Microsoft Research adoption study of Cursor reported a significant, large but transient increase in project velocity, alongside significant and persistent increases in static-analysis warnings and code complexity. The study used an observational difference-in-differences design, so it reports project-level associations rather than a universal outcome for every developer or organization. Taken together, these findings are a reason to measure both delivery and maintenance signals—not to equate more generated code or a faster initial change with a better long-term result.
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What changes for security and code quality
Generated code can introduce vulnerabilities or use an unsafe pattern while appearing reasonable. GitHub’s responsible-use guidance specifically advises caution when using Copilot Chat to generate code for security-sensitive applications and says to review and test generated code thoroughly. An ACM security study also evaluates weaknesses in Copilot code contributions across vulnerability types, prompts, and domains.
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For sensitive changes, include secure coding review, dependency review, secret scanning, and human approval in the normal development process. Review the behavior and trust boundaries, not just whether the code compiles. Teams should also consider whether assistant use fits their privacy and governance requirements, including applicable data handling, retention, enterprise controls, and auditability.
Agentic editing makes these checks more important because one instruction can affect multiple files. A small misunderstanding can propagate across interfaces or configuration, enlarging the blast radius. Keep changes reviewable, use checkpoints or version control, and require explicit review before broad or sensitive changes are accepted.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How to choose an AI coding assistant for your IDE
There is no universally best assistant for VS Code or any other IDE. Compare products against the work your team actually does and the controls it requires.
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- Interaction depth: Does the tool offer inline completion, chat, inline rewrite, or an agent that can take multi-step actions?
- Context reach: Can it use the current file only, open tabs, a repository index, terminal output, issues, or documentation? Can you control what context it receives?
- Review and control: Are changes presented as inspectable diffs? Are checkpoints, undo, approvals, and policy controls available?
- Integration breadth: Does it support the IDEs, languages, frameworks, version-control systems, and CI workflows your project uses?
- Quality evidence: Can your team evaluate test outcomes, static-analysis warnings, escaped defects, and maintenance complexity—not just whether developers like the tool?
- Privacy and governance: Do the data policies, retention terms, enterprise controls, local-model options, and auditability fit your requirements?
- Cost and latency: Check current subscription terms, model routing, usage limits, and response speed against your expected workflow. These details change, so verify them with the vendor before choosing.
For someone asking “Is Cursor better than Copilot?”, the useful comparison is the workflow: the degree of agentic, multi-file editing needed versus the value of IDE-integrated completion and assistance, alongside each product’s context controls and governance. For “Is GitHub Copilot worth it?”, weigh the work it can reliably accelerate against the review and validation time it still requires. A trial in a representative project, assessed with the same quality checks used for ordinary changes, is more informative than choosing by novelty or a single productivity claim.
Will AI replace programmers?
The capabilities described here change how developers produce and review code; they do not establish that programmers will be replaced. Developers still need to decide what should be built, interpret project requirements, evaluate trade-offs, handle ambiguous failures, and take responsibility for changes. As generation expands from a line to a feature-sized task, those judgment and verification skills become more important, not less.
Can AI generate production-ready code?
An assistant can draft code that becomes production-ready after project-specific review and validation, but generated output should not be treated as production-ready merely because it compiles or appears plausible. Production readiness depends on correctness, security, compatibility, maintainability, and the checks required by the system that will run it.
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