How to Write Code with Gemini After the 2026 Shift

I would not tell an individual developer to install Gemini Code Assist in VS Code in July 2026, because that once-obvious answer is now obsolete. The current answer to how to write code with Gemini splits in two: Google Antigravity for individual and consumer workflows, and Gemini Code Assist Standard or Enterprise for organisations that still use supported IDE extensions. That product change is the sharpest fact in this guide, because most tutorials published before 18 June 2026 still describe a free extension that no longer serves consumer requests.

For licensed teams, the core coding experience remains recognisable. Start typing a function and Gemini can offer inline ghost text. Write a precise comment and the assistant can complete the implementation below it. Open the Quick Pick menu and use /generate, /fix, /doc, or /simplify to propose a change, inspect the diff, and accept only the lines that satisfy the task. In VS Code, the transformation shortcut is Control+I on Windows or Linux and Command+I on macOS. In IntelliJ, it is Alt+backslash or Command+backslash. Those actions are fast, but speed is not the same as correctness.

This article therefore treats Gemini as a supervised engineering tool rather than an autonomous author. It shows the current product routes, enterprise setup, VS Code and JetBrains workflows, prompt patterns, context controls, commercial limits, security implications, and a repeatable review sequence. It also separates documented capabilities from historical claims. The generous free completion allowance promoted in February 2025 is useful context, not a current July 2026 entitlement. By the end, a developer or engineering lead should know which Gemini route is actually available, how to produce reviewable code, and where the workflow still requires experienced human judgement.

The June 2026 Product Split Changes the Answer

Google announced the transition on 19 May 2026. Product manager Dmitry Lyalin and principal engineer Taylor Mullen said Gemini CLI had attracted millions of users, more than 100,000 GitHub stars, and 6,000 merged pull requests, but developer needs had shifted towards multi-agent work. Google consequently unified the consumer direction around Antigravity CLI and Antigravity 2.0. The announcement was explicit that consumer Gemini CLI and Gemini Code Assist IDE extensions would stop serving requests on 18 June 2026 for free users and Google AI Pro or Ultra customers.

The enterprise position is different. Organisations using Gemini Code Assist Standard or Enterprise licences retain IDE extension and Gemini CLI access, including model updates. This is not a minor plan distinction. It determines whether a tutorial can be followed at all. An individual who installs an old extension and signs in with a personal account may see the interface yet receive no service. A licensed employee working through an enabled Google Cloud project can still use the IDE path described in the official documentation.

The migration also changes expectations. Lyalin and Mullen warned that there would not be “1:1 feature parity right out of the gate” in Antigravity CLI. Google nevertheless carried forward Agent Skills, Hooks, Subagents, and extensions as plugins, while adding a Go implementation, asynchronous workflows, and a shared agent harness. The practical implication is that an individual developer should not judge Antigravity as a renamed extension. It is a different agent-first environment with a broader orchestration model.

A related deadline applies to GitHub. Current Google Cloud documentation says the consumer Gemini Code Assist on GitHub service will shut down on 17 July 2026. Enterprise code review is a separate product. Teams that rely on automated pull-request summaries or /gemini comments should confirm which service is installed, who owns the Google Cloud project, and whether the repository integration is licensed before treating it as part of a release process.

Choose the Right Gemini Coding Route

The phrase “code with Gemini” now covers three practical routes. The right choice depends less on language preference than on account type, governance, repository scope, and whether the work happens inside an IDE or across multiple agents. The following decision table keeps the distinction operational.

Route	Best Fit	What You Get	Main Constraint
Google Antigravity	Individuals, students, independent developers, Google AI subscribers	Agent-first desktop and CLI workflows, asynchronous multi-agent tasks, retained skills, hooks, subagents, and plugins	Not a one-for-one replacement for the former consumer IDE extension
Gemini Code Assist Standard	Teams needing supported VS Code or JetBrains assistance with enterprise controls	Completion, generation, chat, local context, transformations, agent mode, CLI, and enterprise security features	Requires Google Cloud purchase, licence assignment, API enablement, IAM, and project selection
Gemini Code Assist Enterprise	Large organisations needing private-repository customisation and higher agent allowance	Standard features plus code customisation and higher documented agent-mode or CLI daily requests	Higher price, organisational setup, and a minimum enterprise licence commitment documented in setup guidance
Gemini Code Assist for GitHub	Enterprise pull-request summaries and code review	Automated summaries, review comments, repository context, and /gemini interactions	Consumer version sunsets on 17 July 2026; enterprise service is separate

For a solo developer, the selection should be decisive: start with Antigravity rather than trying to revive an expired consumer extension workflow. For an employee, first ask whether the organisation has assigned a Standard or Enterprise licence. The presence of a Gemini icon in the IDE is not proof that the back-end entitlement, Cloud AI Companion API, project, or IAM permissions are configured.

Standard is the sensible baseline when the objective is code completion, generation, chat, transformations, local codebase awareness, and agent mode. Enterprise becomes relevant when the assistant must draw on indexed private repositories through code customisation, when the team needs the larger agent allowance, or when central governance justifies the commercial step-up. A team should not buy Enterprise merely because its name sounds safer. Both paid editions advertise enterprise-grade security and indemnification. The differentiator is primarily the additional customisation and capacity, not a licence to skip code review.

Set Up Gemini Code Assist for a Licensed Team

A reliable setup starts in Google Cloud, not in the editor marketplace. An administrator purchases the subscription, assigns licences, enables the Cloud AI Companion API, grants the required IAM roles, and identifies the project that developers will use. Only then should developers install the Gemini Code Assist extension in VS Code or a supported JetBrains IDE, sign in with the licensed account, and select the authorised Google Cloud project.

The administrative sequence matters because each failure looks similar inside the IDE. A missing licence, disabled API, blocked endpoint, wrong project, or insufficient role can all appear as a sign-in problem or unavailable feature. Treat setup as a layered checklist rather than repeatedly reinstalling the extension. For enterprise procurement, verify the current minimum licence quantity and commercial terms in the console. Google documentation has also described first-month licence credits for eligible new billing accounts, but such promotions should be checked at purchase time rather than budgeted as permanent pricing.

Network teams need to review outbound access as well. Corporate firewalls, proxies, VPC Service Controls, and user-domain restrictions can stop authentication or model calls even when the extension is correctly installed. Record the approved endpoints, project number, billing account, licence owner, and support path in the team runbook. This converts a personal plugin into a managed development service.

After sign-in, test the smallest possible interaction. Open a disposable repository, create a file in a verified language such as Python, Java, JavaScript, TypeScript, Go, C#, Kotlin, Rust, SQL, or YAML, and request a trivial function. Google lists 22 verified coding languages, while acknowledging that Gemini may understand many others with unevenly tested quality. A successful small completion confirms entitlement and basic connectivity. It does not validate repository context, agent mode, code customisation, or GitHub review, which should each receive a separate acceptance test.

Finally, decide what the assistant may see before inviting it into a production repository. Check .gitignore, create an .aiexclude file where appropriate, exclude secrets, generated artefacts, customer exports, private keys, and licensed third-party source that should not become model context. Governance works best when exclusion rules are committed and reviewed like code, not left as an undocumented setting on one developer’s laptop.

How to Write Code with Gemini in VS Code

Start with Inline Completion

In a licensed VS Code environment, the fastest workflow begins with ordinary typing. Create a new line and type the start of a function, class, query, or test. Gemini can display a suggestion as grey ghost text. Press Tab to accept the proposal or Escape to dismiss it and continue manually. This interaction works best when the file already contains meaningful names, imports, types, and neighbouring patterns. A blank file forces the model to infer too much.

A prompt comment gives the completion a clearer contract. Instead of typing only def, write a short specification above the insertion point. State the inputs, output, error behaviour, and constraints. The comment remains useful documentation even when the suggestion is rejected.

# Validate an email address.
# Return False for empty input, whitespace, or multiple @ symbols.
# Keep the function deterministic and add type hints.

Place the cursor on the next line and pause. Review any ghost-text suggestion for anchoring, Unicode handling, domain assumptions, and catastrophic regular-expression behaviour. Pressing Tab is a code change, not an approval of its design.

Use Quick Pick for Explicit Transformations

For a deliberate generation request, press Control+I on Windows or Linux, or Command+I on macOS. Enter a slash command and a precise instruction. The documented /generate workflow opens a diff so the developer can inspect pending changes before accepting them. That diff view is safer than silently inserting a large block because it turns the model output into an explicit review event.

A productive request is narrower than “build authentication”. Ask for one function, name the framework and version, identify existing helpers, define forbidden dependencies, and request tests. When the file contains selected code, Gemini can apply the transformation to that selection. Keep the first change small enough to understand without scrolling through several unrelated files.

/generate Implement is_valid_email(value: str) -> bool.
Use Python’s standard library only.
Reject leading or trailing whitespace and consecutive dots in the domain.
Do not perform DNS lookups.
Add pytest parameterised tests in test_email_validation.py.

Accept only after running the tests and a formatter. If the assistant creates a dependency, modifies a public API, or broadens the scope, reject the diff and refine the prompt. This loop is usually faster than editing a sprawling first answer.

VS Code also provides the more complete governance controls in Google’s current documentation. Users can disable local codebase awareness, configure .aiexclude behaviour, and decide whether .gitignore participates in context exclusion. That capability is important in regulated or mixed-sensitivity repositories. Capture the chosen settings in onboarding guidance so the same repository is not interpreted differently by each developer.

Use Gemini in IntelliJ and Other JetBrains IDEs

The JetBrains workflow follows the same basic logic but uses different controls. In IntelliJ, press Alt+backslash on Windows or Linux, or Command+backslash on macOS, to open the Gemini quick action menu. Use /generate for a new implementation, /fix for a selected defect, /doc for documentation, and /simplify when the goal is clearer code rather than new behaviour. The proposed change appears for review before acceptance.

JetBrains developers should resist the temptation to treat the assistant as an IDE-wide refactoring engine. Start with a selected method or a tightly bounded file. IntelliJ already has deterministic refactoring tools for renaming, extracting methods, moving classes, and changing signatures. Use those native tools when the transformation is structural and mechanically defined. Use Gemini when the task requires explanation, synthesis, test generation, or a semantic rewrite that the IDE cannot express as a fixed refactor.

The most important 2026 limitation is context control. Google says local codebase awareness is enabled by default and that VS Code users can disable it. The same configuration settings are not supported in IntelliJ and other JetBrains IDEs. Documentation for context exclusions similarly centres configurable settings in supported environments such as VS Code. That does not make JetBrains unsafe, but it changes the governance conversation. A team may need stronger repository-level .gitignore and .aiexclude discipline because developers have fewer local controls.

Another preview limitation concerns next-edit prediction. Where available, it predicts the next likely change after an edit, but documented behaviour is constrained to a single file. Do not expect it to coordinate a schema change across a model, migration, API handler, tests, and documentation. For cross-file work, use a staged plan, agent mode, or explicit prompts per file, then run the full test suite.

A practical IntelliJ sequence is to select a failing method, invoke /fix, ask for the smallest change that satisfies the named test, inspect the diff, and then use /doc only after behaviour is stable. Generating documentation first often freezes an incorrect interpretation into polished prose.

Prompt Patterns That Produce Reviewable Code

Gemini responds to natural language, but a good coding prompt resembles a compact engineering ticket. It gives the model enough context to constrain the solution while preserving room for implementation. The strongest prompts contain five elements: the objective, the existing environment, the behavioural contract, explicit constraints, and the verification request.

Objective names one change. Environment names the language, version, framework, and relevant file or symbol. Contract states inputs, outputs, errors, side effects, and performance expectations. Constraints identify dependencies, security rules, style, compatibility, and files that must not change. Verification asks for tests, edge cases, or a diff explanation. This format reduces plausible but incompatible code.

For example, compare “write a cache” with the following request.

/generate Add an in-memory TTL cache to WeatherClient.get_forecast.
Python 3.12, no third-party packages.
Cache by normalised postcode for 300 seconds.
Do not cache exceptions or HTTP 5xx responses.
Keep the public method signature unchanged.
Use time.monotonic, make tests deterministic with dependency injection,
and add tests for expiry, cache hits, and failed requests.
Explain any thread-safety limitation before the diff.

This prompt gives Gemini a design boundary and makes omissions visible. If the assistant uses time.time, changes the public API, or caches failures, the mismatch is easy to identify. It also surfaces a trade-off that a generic request would hide: an in-memory dictionary may not be safe across threads or useful across processes.

Use follow-up prompts as review comments, not as a stream of approval. Ask “Which requirement does this line satisfy?” or “List the untested failure modes without changing code.” A model is often more useful when asked to criticise its first proposal. For security-sensitive code, split roles: first generate a minimal implementation, then start a new prompt that treats the code as untrusted and searches for injection, authentication, authorisation, data leakage, race conditions, unsafe deserialisation, and dependency risks.

Avoid hidden assumptions. Name the database dialect, deployment target, time zone, character encoding, concurrency model, and backwards-compatibility requirement when they matter. Do not paste entire logs when ten relevant lines and a reproducible command will do. Excess context can bury the actual signal and increase the chance that the response optimises for an irrelevant file.

Finally, request a change plan before code for tasks spanning several components. A six-line plan exposes whether Gemini understands the architecture. Correct the plan, then ask for one step at a time. This reduces the common failure mode in which an agent produces internally consistent files that do not match the system around them.

Fix, Test, Document, and Refactor Without Losing Control

The slash commands are best understood as review accelerators. /fix should start from a reproducible failure. Select the smallest relevant block, include the exact error and failing test, and ask for the minimal correction. A prompt such as “fix this” invites a broad rewrite that may hide the original defect. A stronger request says which behaviour must remain unchanged and which file is allowed to change.

/generate is appropriate for new functions, tests, adapters, scripts, and repetitive scaffolding. Ask for interfaces before implementations when several modules depend on the change. For test generation, require assertions against behaviour rather than simply increasing coverage. A generated test that repeats the implementation’s logic can pass while both are wrong.

/doc can create docstrings, comments, and explanations, but documentation should follow verification. Ask Gemini to state preconditions, side effects, exceptions, thread-safety, and examples. Reject comments that merely translate each line into English. Good documentation explains decisions and contracts that the code cannot express clearly.

/simplify is useful after tests are green. Define what simplicity means: fewer branches, lower cyclomatic complexity, clearer naming, no duplicated parsing, or reduced allocation. Ask the assistant to preserve the public API and produce a behavioural equivalence argument. Then rerun tests, static analysis, benchmarks, and formatting. A shorter function can still be slower, less observable, or harder to debug.

Gemini can also generate unit tests through smart actions and assist with IDE quick fixes. Treat these as proposals from a capable junior collaborator. The developer remains responsible for whether the test suite captures the actual business rule, whether mocks obscure integration behaviour, and whether the fix follows project conventions. The useful productivity gain comes from shortening the distance between hypothesis and executable experiment, not from removing judgement.

Manage Context, Repositories, and File Exclusions

Context determines whether generated code fits the repository. Gemini Code Assist uses the open file and can use tools to locate and read relevant workspace files. Developers can also name files and folders explicitly. Enterprise code customisation can draw on indexed private repositories, which is a different capability from local context and should be governed separately.

A million-token local codebase context limit sounds expansive, but it is not permission to send an entire monorepo indiscriminately. Relevance still matters. Start by naming the interface, implementation, tests, schema, and configuration files that define the task. Exclude vendored dependencies, build artefacts, generated clients, large data fixtures, minified code, and secrets. This improves both privacy and answer quality.

Google documents two exclusion mechanisms. .gitignore can keep common ignored files out of context, while .aiexclude uses the same pattern syntax and is designed for AI context control. For code customisation, .aiexclude is the controlling file and its directives take precedence in conflicts. That distinction is easy to miss. A repository can intentionally track a sensitive fixture for deployment while still excluding it from AI context.

Do not assume a local setting protects every interface. IDE completion, chat, agent mode, CLI, GitHub review, and enterprise repository customisation may obtain context through different paths. Create a data-flow inventory that maps each feature to its source files, project, licence, logs, and exclusions. Test it with canary files containing harmless unique strings. Ask Gemini about the canary, then confirm whether the exclusion behaves as intended.

Remote repository context is powerful for Enterprise teams because it can ground an answer in private code beyond the currently open workspace. It also raises provenance questions. Require the assistant to name the files or symbols it relied on, and verify that generated patterns match the current branch rather than an indexed older version. When the source of a suggestion is unclear, treat it as unverified even if it compiles.

Pricing, Quotas, and Hidden Plan Limits

Google publishes Gemini Code Assist Standard and Enterprise pricing as hourly licence rates tied to monthly or annual commitments. Converting the displayed rates with a 730-hour nominal month produces useful planning equivalents, but these are calculations rather than vendor-displayed monthly totals. Standard is approximately US$22.80 per user per month on a monthly commitment and US$19 on an annual commitment. Enterprise is approximately US$54 and US$45 respectively. Taxes, contractual terms, regional billing, credits, and minimum quantities can change the realised cost.

Quotas are per user in a Google Cloud project. Google documents two requests per second, 6,000 daily code requests, and 960 daily chat and related requests. Standard has a documented allowance of 1,500 agent-mode or Gemini CLI requests per day, while Enterprise has 2,000. A single high-level agent request can trigger multiple model interactions, so the allowance is not equivalent to 1,500 or 2,000 completed tasks.

Item	Standard	Enterprise	Operational Meaning
Monthly commitment rate	$0.031232877 per hour, about $22.80 per 730-hour month	$0.073972603 per hour, about $54 per 730-hour month	Calculated monthly equivalents; confirm invoice terms in the Cloud console
Annual commitment rate	$0.026027397 per hour, about $19 per 730-hour month	$0.061643836 per hour, about $45 per 730-hour month	Lower rate with annual commitment
Code requests per day	6,000	6,000	Generation and completion requests per user in a project
Chat and related requests per day	960	960	Includes responses displayed in IDE and Cloud Assist panels
Agent mode or CLI requests per day	1,500	2,000	One user prompt may consume several underlying model requests
Local codebase context	Up to 1,000,000 tokens	Up to 1,000,000 tokens	Capacity does not remove the need for relevance and exclusions
Code customisation repositories	Not included	Up to 20,000 repositories	Enterprise grounding across indexed private repositories

The hidden commercial issue is not simply price. Enterprise setup guidance has documented a minimum licence commitment, while promotions such as introductory credits may be temporary and eligibility-dependent. Procurement should request the current order screen, cancellation rules, reassignment behaviour, support terms, and whether idle licences continue billing. Engineering should separately monitor request usage and failure rates so that quota pressure is not mistaken for model unreliability.

Performance bottlenecks occur at several layers. Two requests per second can affect rapid automated workflows. Large context can increase latency and make grounding less precise. Agent mode may spend several calls exploring a repository before producing a diff. Firewall inspection, proxy authentication, project misconfiguration, and extension updates can add their own delays. Measure time to first suggestion, time to accepted change, rejected-diff rate, tests added, escaped defects, and review effort. Completion count alone rewards volume rather than engineering value.

Security, Privacy, Provenance, and Human Review

Google Cloud states that prompts and responses are handled under its terms and that customer prompts and responses are not used to train models. It also describes encryption, governance, and controls for private code used in customisation. Those commitments are important, but they do not make every prompt appropriate. The organisation remains responsible for access control, repository classification, secret handling, retention, logging, and the code it accepts.

The strongest independent caution comes from developer studies. A March 2026 security experiment involving 159 participants found no statistically significant improvement in secure software development for the Gemini condition, while developer experience still improved code security. The authors concluded that experience “cannot be fully substituted by Gemini”. The result does not prove that Gemini is harmful. It shows that assistance does not erase the value of threat modelling, secure-design knowledge, and careful review.

A separate 2026 empirical study analysed roughly 3,800 public bugs across Claude Code, Codex, and Gemini CLI. More than two thirds involved functionality, with API, integration, and configuration problems forming a large share. Tool invocation and command execution were prominent failure locations. These findings match a practical observation: agentic coding often fails at boundaries, where credentials, shell state, APIs, dependencies, and repository assumptions meet.

Longitudinal research adds a human-factor warning. In a study with 95 matched respondents across two surveys, 82 per cent reported spending less time writing code and 84 per cent perceived a productivity improvement, yet the share reporting a worsened developer experience rose from 14 to 27 per cent. Researchers Annie Vella and Kelly Blincoe described the shift as “supervisory engineering work”. Teams gain output but may also inherit more review, coordination, and uncertainty.

A safe review gate should include compilation, unit and integration tests, static analysis, dependency scanning, secret scanning, licence checks, formatting, performance tests where relevant, and a human diff review. For authentication, cryptography, payments, access control, infrastructure, migrations, and destructive operations, require an experienced reviewer who understands the system. Ask Gemini to cite source locations when it reuses repository patterns, and investigate any unfamiliar algorithm or dependency before merging.

Risk	What to Check	Minimum Control
Functional mismatch	Inputs, outputs, errors, state changes, concurrency, compatibility	Tests derived from requirements, not from generated implementation
Security defect	Injection, authorisation, secrets, unsafe parsing, race conditions, dependency risk	Threat-informed human review plus security tooling
Context leakage	Files visible to IDE, agent, CLI, GitHub, and customisation	Repository classification, .aiexclude, .gitignore, access control, canary tests
Provenance or licence issue	Unexpected idioms, copied comments, source citations, package licence	Source inspection and software composition analysis
Operational failure	Quota, network, IAM, project, extension version, rate limits	Telemetry, runbook, bounded retries, and support ownership
Review fatigue	Large diffs, repeated regeneration, low rejection discipline	Small changes, diff limits, and independent reviewer for high-risk code

A Practical End-to-End Gemini Coding Workflow

The most reliable workflow is intentionally staged. It uses Gemini to accelerate understanding and drafting while preserving deterministic tools and human ownership.

1. Define one outcome. Write the expected behaviour, interfaces, constraints, and acceptance tests before opening the prompt.
2. Select the supported route. Use Antigravity for individual workflows or confirm a Standard or Enterprise licence, project, API, and IAM for the IDE extension.
3. Prepare context. Open only the relevant files, identify the target symbol, and review .gitignore and .aiexclude rules. Remove secrets and irrelevant artefacts.
4. Ask for a plan. For multi-file work, require a short ordered plan, affected files, risks, and unanswered questions before code generation.
5. Generate the smallest diff. Use ghost text for a local completion or /generate for a reviewed transformation. Do not accept broad incidental changes.
6. Interrogate the proposal. Ask Gemini to list assumptions, failure modes, complexity, thread-safety, compatibility, and untested branches without changing code.
7. Run deterministic checks. Compile, format, lint, test, scan dependencies and secrets, and execute relevant benchmarks. Capture the exact commands and results.
8. Perform human review. Compare the diff against the original requirement and architecture. Reject code that is merely plausible, clever, or well explained.
9. Document verified behaviour. Use /doc only after tests pass, then correct any claim that is not guaranteed by the implementation.
10. Measure the workflow. Track accepted-diff rate, time saved after review, escaped defects, review time, and developer experience rather than raw suggestion volume.

Consider a failing email validator. First reproduce the bug with a test containing leading whitespace and a consecutive-dot domain. Select only the validator and test, invoke /fix, and ask for the smallest standard-library change. If Gemini replaces the implementation with an elaborate regular expression, reject it and request a parser-based approach or a narrower rule set. Run the tests, add counterexamples, and inspect performance on long adversarial input. Only then ask /doc to describe exactly what the validator does and does not guarantee.

The sequence matters because it turns model uncertainty into observable checkpoints. A weak result is cheap to discard at the plan or small-diff stage. The same weak result becomes expensive after it spreads across migrations, APIs, tests, and documentation. Gemini is most productive when the team makes rejection easy.

Our Content Testing Methodology

This guide used a documentation-led technical verification process dated 14 July 2026. We checked Google’s May 2026 product-transition announcement against current Gemini Code Assist overview, coding workflow, supported-language, pricing, quota, data-governance, local-context, exclusion, and GitHub-review documentation. We calculated nominal monthly pricing equivalents directly from the published hourly rates using 730 hours and labelled those figures as calculations rather than official monthly totals.

We mapped the documented VS Code and IntelliJ commands to a reproducible sample workflow built around a typed Python email validator. We inspected the prompt for testability, scope, dependency control, error behaviour, and review checkpoints. We did not claim a live licensed performance benchmark, because no authenticated enterprise tenant was available in this editorial environment. Latency, model quality, and extension behaviour should therefore be acceptance-tested in the reader’s own project and IDE version.

We cross-referenced security and engineering claims with three 2026 research papers: an empirical bug study of AI coding tools, a controlled study of Gemini and developer experience in secure development, and a longitudinal survey of AI coding-assistant use. We treated survey perceptions as perceptions, not measured productivity, and separated historical 2025 consumer claims from current July 2026 availability.

Required publication disclosure once the named human desk has completed its review: “This article was researched and drafted with AI assistance and reviewed by the Sami Ullah Khan editorial desk at Perplexity AI Magazine. All data, citations, pricing figures, and named quotes have been independently verified against primary sources before publication.” At draft delivery, that named human editorial review remains pending and must be completed before publication.

The live perplexityaimagazine.com sitemap and its stated fallback endpoints did not return verifiable article URLs during this research session. We therefore inserted no fabricated internal links. The publisher must add six to eight relevant internal links from a confirmed sitemap and run the final technical compliance checks after the WordPress page is published.

Conclusion

The useful way to write code with Gemini in 2026 begins by choosing the product that still serves the account. Individuals now belong on Google Antigravity, while organisations can continue using Gemini Code Assist Standard or Enterprise in VS Code and supported JetBrains IDEs. Inside those licensed environments, ghost-text completion, prompt comments, slash commands, diff review, local context, agent mode, and enterprise repository grounding can remove repetitive work and shorten experimentation.

The limits are equally important. Consumer tutorials became outdated on 18 June. JetBrains provides less local-context configuration than VS Code. Quotas, rate limits, repository indexing, and multi-call agents can constrain heavy workflows. Independent studies show meaningful perceived productivity gains, but also boundary failures, review burden, and no substitute for developer experience in secure work.

The open question is not whether Gemini can produce code. It plainly can. The harder question is whether a team can make its use observable, reversible, testable, and accountable. Small diffs, explicit contracts, repository exclusions, deterministic checks, and experienced review turn an impressive suggestion engine into a controlled engineering tool. Without those controls, faster generation can simply move cost downstream into debugging, security review, and maintenance.

Frequently Asked Questions

Can Individuals Still Use Gemini Code Assist in VS Code?

Not through the former consumer service. Google said Gemini Code Assist IDE extensions stopped serving requests for free individual users and Google AI Pro or Ultra customers on 18 June 2026. Individual developers should use Google Antigravity. Organisations with Gemini Code Assist Standard or Enterprise licences retain supported IDE access.

How Do I Generate Code with Gemini in VS Code?

In a licensed environment, press Control+I on Windows or Linux, or Command+I on macOS, to open Quick Pick. Enter /generate followed by a specific task, inspect the proposed diff, and accept only after running tests and checks. Inline ghost text can also be accepted with Tab or dismissed with Escape.

How Do I Use Gemini in IntelliJ?

Install the licensed Gemini Code Assist plugin, sign in, and select the authorised Google Cloud project. Press Alt+backslash on Windows or Linux, or Command+backslash on macOS, to open quick actions. Use commands such as /generate, /fix, /doc, and /simplify, then review the diff before acceptance.

What Programming Languages Does Gemini Code Assist Support?

Google says the models may assist with many public programming languages, but it verifies quality for 22 languages including Python, Java, JavaScript, TypeScript, Go, C, C++, C#, Kotlin, Rust, SQL, Swift, Bash, and YAML. Quality can still vary by framework, version, repository context, and task complexity.

What Are the Daily Gemini Code Assist Limits?

Google documents two requests per second, 6,000 daily code requests, and 960 daily chat and related requests per user in a project. Standard has 1,500 daily agent-mode or CLI requests, while Enterprise has 2,000. A high-level agent task may consume several underlying model requests.

Does Google Train Gemini on Enterprise Prompts and Code?

Google Cloud states that customer prompts and responses are not used to train models and describes contractual data handling and security controls. Organisations must still manage access, exclusions, secrets, retention, logs, and repository classification. Vendor commitments do not remove the customer’s responsibility for safe prompt content and accepted code.

Is Gemini-Generated Code Safe to Use in Production?

It can be used only after normal engineering review. Compile it, run unit and integration tests, apply static and dependency analysis, scan for secrets, inspect licences, and review the diff. Security-sensitive code requires experienced human review. A 2026 study found developer experience could not be fully substituted by Gemini.

Should I Choose Standard or Enterprise?

Choose Standard for enterprise IDE completion, generation, chat, transformations, local context, agent mode, and CLI. Choose Enterprise when private-repository code customisation, higher agent allowance, or central large-scale grounding is required. Confirm current pricing, licence minimums, contractual terms, and repository governance before purchase.

References

1. Google Developers. (2026, May 19). An important update: Transitioning Gemini CLI to Antigravity CLI.

2. Google for Developers. (2026). Code with Gemini Code Assist.

3. Google for Developers. (2026). Supported languages, IDEs, and interfaces.

4. Google Cloud. (2026). Gemini for Google Cloud pricing.

5. Google Cloud. (2026). Quotas and limits.

6. Google Cloud. (2026). How Gemini for Google Cloud uses your data.

7. Jost, P., Beckers, K., Scandariato, R., & Weir, C. (2026). The impact of AI-assisted development on software security: A study of Gemini and developer experience. arXiv.

8. Vella, A., & Blincoe, K. (2026). The impact of AI coding assistants on software engineering: A longitudinal study. arXiv.

9. Zhang, Y., et al. (2026). Engineering pitfalls in AI coding tools: An empirical study of bugs in Claude Code, Codex, and Gemini CLI. arXiv.