Mastering Gemini 3's 1 Million Token Context Window: Advanced Strategies (2025)

Google Antigravity's Gemini 3 model features an unprecedented 1 million token context window—the largest in any AI coding assistant. This comprehensive guide teaches you how to leverage this massive context for complex coding tasks that were previously impossible.

Understanding the 1 Million Token Advantage

What Does 1 Million Tokens Mean?

To put this in perspective:

Context Size	Approximate Code	Use Case
8K tokens	~200 lines	Single file editing
32K tokens	~800 lines	Small component work
128K tokens	~3,200 lines	Multi-file features
1M tokens	~25,000 lines	Entire codebase analysis

With 1 million tokens, Gemini 3 can process:

• Your entire medium-sized codebase at once
• Complete application architectures
• Full documentation alongside code
• Extensive conversation history

Why Context Size Matters

Larger context enables:

1. Holistic Understanding: See how components interact across files
2. Consistent Refactoring: Maintain patterns across the entire codebase
3. Better Suggestions: More context = more accurate completions
4. Complex Tasks: Multi-step operations without losing track

Strategy 1: Intelligent Context Loading

Automatic Context with @codebase

Google Antigravity's @codebase command intelligently loads relevant files:

@codebase How does the authentication flow work?

Gemini 3 will:

1. Scan your project structure
2. Identify auth-related files
3. Load them into context
4. Provide comprehensive analysis

Manual Context Selection

For precise control, manually specify files:

@src/lib/auth.ts @src/middleware.ts @src/app/api/auth/[...nextauth]/route.ts

Explain how these files work together for authentication.

Context Priorities

When approaching the context limit, prioritize:

1. Core business logic (highest priority)
2. Type definitions (helps accuracy)
3. Related utilities (supporting context)
4. Test files (if discussing testing)
5. Config files (lowest priority)

Strategy 2: Large-Scale Refactoring

The Power of Full-Codebase Awareness

Traditional AI assistants lose track during multi-file refactors. With 1M tokens:

@codebase

I want to rename the `getUserData` function to `fetchUserProfile` 
across the entire codebase. This includes:
- The function definition
- All import statements
- All call sites
- Related type definitions
- Test files
- Documentation references

Show me a complete refactoring plan.

Step-by-Step Large Refactors

For massive changes, break into phases:

Phase 1: Analysis

@codebase

Analyze all usages of the deprecated `moment.js` library.
List every file, the specific usage, and suggest the dayjs equivalent.

Phase 2: Planning

Based on the analysis, create a migration plan that:
1. Prioritizes high-impact files
2. Groups related changes
3. Identifies potential breaking changes
4. Suggests test coverage additions

Phase 3: Execution

Execute phase 1 of the migration plan:
- Update @src/utils/dateHelpers.ts
- Update all components importing from dateHelpers
- Ensure TypeScript types are correct

Strategy 3: Architecture Analysis & Documentation

Understanding Complex Systems

Load entire modules for architectural analysis:

@src/services/ @src/lib/ @src/types/

Document the service layer architecture:
1. Service dependencies graph
2. Data flow patterns
3. Error handling approach
4. Suggested improvements

Generating Architecture Diagrams

@codebase

Generate a Mermaid diagram showing:
1. All React components and their relationships
2. Data flow from API to UI
3. State management connections

Example output:

graph TD
    A[App] --> B[Dashboard]
    B --> C[UserStats]
    B --> D[RecentActivity]
    C --> E[useUser Hook]
    D --> E
    E --> F[Supabase Client]
    F --> G[(Database)]

Strategy 4: Cross-Repository Understanding

Working with Monorepos

In monorepo setups, load multiple packages:

@packages/ui @packages/shared @apps/web/src/components

Create a new Button component in packages/ui that:
1. Follows the existing design system patterns
2. Uses shared utilities from packages/shared
3. Is compatible with how components are used in apps/web

Maintaining Consistency

@codebase

Audit all components for design system consistency:
- Color usage (should use CSS variables)
- Spacing (should use spacing scale)
- Typography (should use text styles)
- Border radius (should match tokens)

List all violations with file:line references.

Strategy 5: Test Generation at Scale

Comprehensive Test Coverage

@src/lib/payments/ @src/types/payment.ts

Generate comprehensive tests for the payments module:
1. Unit tests for each function
2. Integration tests for Stripe webhooks
3. Edge cases from the type definitions
4. Mock data based on actual types

Test Migration

@**/*.test.js @jest.config.js

I'm migrating from Jest to Vitest. 
Analyze all 47 test files and provide:
1. Compatibility issues list
2. Required syntax changes
3. Config migration steps
4. A script to automate simple transformations

Strategy 6: Intelligent Code Review

Full PR Context Review

@git:diff:main

Review this PR with full codebase context:
1. Does it follow existing patterns?
2. Are there any inconsistencies with similar code?
3. Missing test coverage?
4. Potential performance issues?
5. Security concerns?

Historical Context

@git:history:src/lib/auth.ts @src/lib/auth.ts

This file has had 3 bugs in the last month.
Analyze the commit history and current code to:
1. Identify root cause patterns
2. Suggest preventive measures
3. Recommend architectural improvements

Strategy 7: Documentation Generation

Auto-Generate Complete Documentation

@codebase

Generate comprehensive API documentation including:
1. All exported functions with signatures
2. Usage examples for each
3. Error handling documentation
4. Related functions cross-references
5. Migration notes from v1 to v2

README Generation

@codebase @package.json @.env.example

Generate a production-ready README.md with:
- Project overview (inferred from code)
- Installation steps
- Environment variables (from .env.example)
- API documentation
- Deployment guide
- Contributing guidelines

Context Management Best Practices

Practice 1: Progressive Loading

Start focused, expand as needed:

# Start specific
@src/components/Dashboard.tsx

# If needed, expand
@src/components/Dashboard.tsx @src/hooks/useDashboard.ts

# If still needed, go broader
@src/components/ @src/hooks/

Practice 2: Context Checkpoints

For long sessions, periodically summarize:

Summarize our progress so far:
1. What files have we modified?
2. What decisions have we made?
3. What remains to be done?

Store this as a checkpoint.

Practice 3: Exclude Irrelevant Files

Use .antigravityignore to exclude noise:

# .antigravityignore
node_modules/
.next/
coverage/
*.min.js
*.map
dist/
build/

Practice 4: Context Budgeting

Know your limits:

How much context am I using?

/context

Antigravity will show:

• Current token usage
• Files loaded
• Conversation history size
• Remaining capacity

Real-World Scenarios

Scenario 1: Legacy Code Modernization

@src/legacy/ @src/types/ @src/modern/

I'm modernizing a legacy Express.js API to Next.js API routes.

The legacy code in @src/legacy has 45 endpoints.
Modern patterns are demonstrated in @src/modern.

Create a migration plan that:
1. Maps each legacy endpoint to its modern equivalent
2. Identifies shared logic to extract
3. Prioritizes based on usage (check route handlers)
4. Estimates effort for each migration

Scenario 2: Security Audit

@codebase

Perform a security audit:
1. SQL injection vulnerabilities
2. XSS possibilities
3. Authentication bypasses
4. Sensitive data exposure
5. Dependency vulnerabilities

For each finding, provide:
- Severity (Critical/High/Medium/Low)
- File:line location
- Exploit scenario
- Recommended fix

Scenario 3: Performance Optimization

@codebase

Identify performance bottlenecks:
1. N+1 query patterns
2. Unnecessary re-renders
3. Missing memoization
4. Large bundle imports
5. Unoptimized images
6. Missing caching

Prioritize by impact and provide fix recommendations.

Frequently Asked Questions

Does using more context slow down responses?

Slightly. Processing 1M tokens takes longer than 8K, but Antigravity optimizes by:

• Intelligent chunking
• Parallel processing
• Caching repeated context

Can I exceed 1 million tokens?

The hard limit is 1M tokens. If you hit it:

1. Use .antigravityignore to exclude files
2. Be more selective with @ commands
3. Clear conversation history: /clear

How do I know what's in my context?

Use the /context command to see:

• Loaded files
• Token counts per file
• Conversation history size

Is my code sent to Google's servers?

Yes, code is processed by Gemini 3. For sensitive projects:

• Check your organization's AI policy
• Consider enterprise plans with data residency
• Use local models for classified work

Can I save and restore context?

Yes! Use context snapshots:

/save-context auth-refactor
/load-context auth-refactor

Conclusion

Gemini 3's 1 million token context window is a game-changer for AI-assisted development. By mastering these strategies, you can:

• Analyze entire codebases holistically
• Execute complex multi-file refactors
• Generate comprehensive documentation
• Perform thorough security audits
• Modernize legacy systems systematically

Start small, expand progressively, and always budget your context wisely. The developers who master large-context AI will have a significant productivity advantage.

Ready to put these strategies into practice? Explore our .antigravity rules templates or connect your first MCP server for even more capabilities.

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Last updated: December 2024