Best AI for Writing Code in 2025: Pick the Right Assistant

The best AI for writing code depends on your stack, governance needs, and appetite for automation. This 2025 guide matches assistants to frontend, backend, and data workflows, while explaining how research helpers like Google Antigravity support requirements and design without handling source. Pair assistants with Propel Code review so policies, tests, and evidence stay consistent. It links to deeper dives on review automation and broader AI dev tooling.

Frontend teams

Cursor and Windsurf excel at component refactors and test scaffolds. Combine them with Playwright for end-to-end coverage. Keep accessibility and performance checks in CI so AI generated UI changes stay compliant.

Backend teams

JetBrains AI and VS Code with Copilot handle typed services well. Use Propel to enforce API contracts and security checks before merge. Add contract tests to ensure AI generated code respects schemas.

Data and ML teams

Prefer assistants that can reason about notebooks and pipelines. Keep lineage and data access controls in place. For model experiments, pair AI suggestions with evals and cost monitoring.

Research support with Antigravity

Use Antigravity to summarize design docs, RFCs, and vendor updates. Keep code out of research prompts and treat it as a requirements aide that feeds clean inputs to your IDE assistant.

Buyer checklist

• SSO, audit logs, and data residency controls
• Repo-aware context and local indexing options
• Clear diffs and explanations for every suggestion
• CI integration plus policy enforcement via Propel
• Support for your testing stack and languages

Language- and workflow-specific picks

• Frontend (React/Vue): Cursor or Windsurf for component refactors and Playwright scaffolds.
• Backend (TypeScript/Java/Kotlin): JetBrains AI or VS Code with Copilot plus strict linting.
• Data/ML: Assistants that handle notebooks and pipelines; pair with evals and lineage.
• Monorepos: Tools with fast cross-repo indexing and code search to avoid stale context.

Quality measurement loop

1. Track acceptance rate of suggestions per language and repo.
2. Monitor escaped defects tied to AI-authored lines (tag in PR descriptions).
3. Record review latency and PR cycle time before and after adoption.
4. Sample 10 percent of AI-heavy PRs for manual quality audits monthly.

Prompt and guardrail patterns

Use prompts that spell out acceptance criteria, tests to add, and security constraints. Keep prompts short, avoid secrets, and reference internal guidelines stored in your knowledge base. Require AI edits to pass lint, type checks, and CI tests before merge.

Enterprise vs startup considerations

• Startups: prioritize speed, but keep linting and tests strict; upgrade when quotas block CI.
• Enterprises: require SSO, SCIM, audit logs, data residency, and exportable evidence.
• Both: enforce policy packs in Propel and keep deterministic scanners as blockers.

FAQ

Which assistant is best for large monorepos?

Choose tools with strong indexing and navigation like Cursor or JetBrains AI. Keep caching local to reduce latency and enforce branch protections with Propel.

How do I avoid noisy suggestions?

Tune prompt settings, prefer typed languages where possible, and prune plugins you do not use. Collect feedback on suggestion acceptance to adjust defaults.

If you want AI code suggestions that respect your standards, layer Propel review on every PR so policies, tests, and security checks stay intact while assistants speed up authoring.

Sources and further reading

• VS Code Copilot overview for configuration, privacy, and suggested workflows.
• JetBrains AI Assistant docs for typed-language and monorepo guidance.
• OWASP ASVS to keep AI generated code aligned with security verification requirements.