Large Codebase Management: Tips 36-50

Working with large codebases requires different strategies than small projects. These 15 tips will help you leverage Claude Code’s unique capabilities for enterprise-scale development, from million-line monorepos to complex microservice architectures.

Understanding Claude Code’s Strengths

Tip 36: Leverage Claude’s Codebase Awareness

Claude Code uses agentic search to understand your entire project structure automatically:

# Instead of manually explaining project structure
"Explain how the authentication system works"

# Claude automatically:
# 1. Searches for auth-related files
# 2. Identifies key components
# 3. Traces dependencies
# 4. Understands the flow
# 5. Provides comprehensive explanation

Agentic Search Capabilities

• Pattern Recognition: Finds similar code patterns across files
• Dependency Tracing: Understands import chains and relationships
• Context Building: Automatically gathers relevant context
• Smart Filtering: Focuses on important files, ignores noise
• Cross-Reference: Links related functionality across modules

Real-world example from Anthropic’s Data Infrastructure team:

# New hire onboarding
"Help me understand how data flows from our API to the dashboards"

# Claude's response includes:
# - API endpoint identification
# - Data transformation pipeline
# - Database schema relationships
# - Dashboard query patterns
# - Relevant CLAUDE.md documentation

Tip 37: Handle Extremely Large Files

Claude Code excels where other tools fail with massive files:

18,000+ Line Files

# Builder.io's React component example
"Update the event handler in our 18,000 line component"

# Claude successfully:
# - Loads entire file
# - Finds specific function
# - Makes precise changes
# - Maintains context
# - Handles complex state

Performance Tips

# For optimal performance with large files:

# 1. Be specific about location
"Update the handleSubmit function around line 8500"

# 2. Use search patterns
"Find all instances of deprecated API calls"

# 3. Work incrementally
"First show me the authentication logic"
"Now update the error handling"

Wskazówka

Claude Code can handle files that crash other AI coding tools. Files with 10,000+ lines are processed smoothly.

Tip 38: Use Agentic Search for Navigation

Let Claude navigate complex codebases for you:

# Finding patterns
"Show me all places where we handle user permissions"

# Understanding architecture
"How do our microservices communicate?"

# Locating functionality
"Where is the email validation logic?"

# Dependency analysis
"What depends on the UserService class?"

# Cross-cutting concerns
"Find all database queries that might have N+1 problems"

Advanced search examples:

# Complex pattern matching
"Find all React components that directly access localStorage"

# Architecture validation
"Check if any frontend components import from backend modules"

# Security audit
"Locate all places where we construct SQL queries"

# Performance analysis
"Find all synchronous file operations in our async handlers"

Tip 39: Break Down Complex Tasks

Structure large refactoring projects effectively:

1. Initial Analysis

   "Analyze the current authentication system and identify areas for improvement"

2. Create a Plan

   "Create a step-by-step plan to migrate from session-based to JWT authentication"

3. Implement Incrementally

   "Step 1: Create new JWT utility functions"
   "Step 2: Update user model to support refresh tokens"
   "Step 3: Modify login endpoint"

4. Verify Each Step

   "Write tests for the JWT utilities"
   "Verify backward compatibility"

Task Breakdown Strategy

• Size Limit: Keep each task under 200 lines of changes
• Test First: Write tests before implementation
• Checkpoint: Commit after each successful step
• Rollback Plan: Always have a way to revert
• Document: Update CLAUDE.md with decisions

Tip 40: Provide Code in Focused Chunks

Optimize Claude’s performance with targeted context:

# Less effective: Vague request
"Optimize our application"

# More effective: Focused request
"Optimize the database queries in the UserRepository class"

# Even better: Specific context
"The getUsersWithOrders method in UserRepository has N+1 query issues.
 Optimize it using eager loading."

Context strategies for large codebases:

• 10k lines: Claude’s sweet spot for comprehensive understanding
• 50k lines: Still manageable but focus on specific modules
• 100k+ lines: Break into logical segments and work incrementally

Parallel Development Strategies

Tip 41: Use Multiple Instances for Different Areas

Run parallel Claude Code instances for maximum efficiency:

# Terminal 1: Frontend development
claude --add-dir ./frontend
"Implement new user dashboard"

# Terminal 2: Backend API
claude --add-dir ./backend
"Create REST endpoints for dashboard data"

# Terminal 3: Database migrations
claude --add-dir ./database
"Design schema for dashboard features"

# Terminal 4: Testing
claude --add-dir ./tests
"Write integration tests for new features"

Notatka

Each instance maintains its own context, allowing true parallel development without context pollution.

Benefits of parallel instances:

• No Context Switching: Each instance stays focused
• Team Simulation: Work like a team of developers
• Faster Development: Complete tasks simultaneously
• Better Organization: Clear separation of concerns

Tip 42: Leverage Filesystem as Shared Workspace

Use the filesystem for collaboration between instances:

shared/types/api.types.ts

# Instance 1: Generate types
"Generate TypeScript interfaces from our API responses"
# Instance 2: Use generated types
"Create React hooks using the types in shared/types/"

# Instance 3: Documentation
"Document the types in shared/types/ with examples"

Shared Workspace Patterns

project/
├── .claude/
│   ├── generated/    # AI-generated code
│   ├── templates/    # Reference implementations
│   └── workspace/    # Shared working files
├── docs/
│   └── ai-sessions/  # Session documentation

Real-world workflow:

# Pull reference implementation
"Copy the authentication logic from that popular npm package into
 .claude/templates/ so we can reference their patterns"

# Use as reference
"Implement similar authentication but adapted for our patterns"

Tip 43: Implement Hierarchical CLAUDE.md Files

Structure documentation for large monorepos:

monorepo/
├── CLAUDE.md                    # Global rules and patterns
├── packages/
│   ├── frontend/
│   │   ├── CLAUDE.md           # Frontend-specific
│   │   └── src/
│   │       └── components/
│   │           └── CLAUDE.md   # Component guidelines
│   ├── backend/
│   │   ├── CLAUDE.md           # Backend patterns
│   │   └── src/
│   │       ├── services/
│   │       │   └── CLAUDE.md   # Service patterns
│   │       └── models/
│   │           └── CLAUDE.md   # Data model rules
│   └── shared/
│       └── CLAUDE.md           # Shared code rules

Example hierarchical documentation:

Root CLAUDE.md

# Monorepo Overview

## Architecture Principles
- Microservices with shared libraries
- Event-driven communication
- TypeScript throughout

## Global Standards
- Conventional commits
- 100% test coverage for shared code
- No circular dependencies

Service CLAUDE.md

# User Service

## Responsibilities
- User authentication
- Profile management
- Permission handling

## Dependencies
- Shared auth library
- Database service
- Event bus

## Patterns
- Repository pattern for data access
- JWT for authentication
- Event sourcing for audit

Tip 44: Document Architecture Patterns

Help Claude understand your system design:

# Architecture Documentation

## System Overview
```mermaid
graph TD
    A[API Gateway] --> B[User Service]
    A --> C[Product Service]
    A --> D[Order Service]
    B --> E[PostgreSQL]
    C --> F[MongoDB]
    D --> E
    D --> G[Redis Cache]
    B --> H[Event Bus]
    C --> H
    D --> H

Design Patterns

1. Repository Pattern: All database access through repositories
2. CQRS: Separate read/write models for complex domains
3. Event Sourcing: Audit trail for critical operations
4. Circuit Breaker: For external service calls
5. Saga Pattern: For distributed transactions

Communication Patterns

• Sync: REST APIs with OpenAPI specs
• Async: RabbitMQ for events
• Real-time: WebSockets for live updates

Data Flow

1. Client → API Gateway (authentication)
2. Gateway → Microservice (authorized request)
3. Service → Database (data operation)
4. Service → Event Bus (state change)
5. Other Services → Event Bus (react to changes)

### Tip 45: Use Context-Aware Queries

Ask sophisticated questions about code relationships:

```bash
# Dependency analysis
"Show me all services that depend on the User model"
"What would break if I change the authenticate method signature?"
"Find circular dependencies in our import structure"

# Performance analysis
"Identify all database queries in hot code paths"
"Find synchronous operations that could be async"
"Show me all uncached expensive computations"

# Security audit
"Find all user input that isn't validated"
"Show me everywhere we construct dynamic SQL"
"Identify exposed sensitive data in API responses"

# Architecture validation
"Verify all services follow our repository pattern"
"Find direct database access outside of repositories"
"Check if any frontend code imports backend modules"

Token and Performance Optimization

Tip 46: Optimize for Token Efficiency

Manage token usage in large projects:

Token-Saving Strategies

# 1. Clear frequently
/clear

# 2. Focus conversations
"Work only on the authentication module"

# 3. Use specific file references
@auth/login.service.ts
# Instead of: "the login service file"

# 4. Compress context
/compact

# 5. Remove unnecessary files
"Ignore test files for this task"

Token Usage Patterns

Typical token usage by task:

- Simple bug fix: 2K-5K tokens
- Feature implementation: 10K-20K tokens
- Large refactoring: 50K-100K tokens
- Architecture analysis: 20K-50K tokens

Cost optimization:
- Use Sonnet for routine tasks
- Reserve Opus for complex work
- Clear between unrelated tasks
- Focus on specific modules

Tip 47: Create Module-Specific Documentation

Document each major module comprehensively:

# Payment Module Documentation

## Overview
Handles all payment processing including credit cards,
PayPal, and cryptocurrency payments.

## Key Files
- `payment.service.ts` - Main service orchestrator
- `processors/` - Payment processor implementations
- `models/transaction.model.ts` - Transaction data model
- `webhooks/` - Payment provider webhooks

## Critical Business Logic
1. **Retry Logic**: 3 attempts with exponential backoff
2. **Idempotency**: Use transaction_id to prevent duplicates
3. **Audit Trail**: Every operation logged to audit_log table
4. **Refunds**: Max 90 days, requires manager approval

## Integration Points
- User Service: For customer data
- Order Service: For order fulfillment
- Notification Service: For payment receipts
- Audit Service: For compliance logging

## Testing Requirements
- Unit tests: Mock all external providers
- Integration tests: Use sandbox environments
- Load tests: 1000 TPS minimum
- Security tests: PCI compliance required

## Common Issues
1. Webhook timeouts - implement async processing
2. Currency conversion - cache rates for 1 hour
3. Failed payments - clear user communication
4. Partial refunds - complex state management

Tip 48: Use Incremental Refactoring

Approach large refactoring systematically:

1. Analyze Current State

   "Analyze the authentication system and create a refactoring plan"

2. Create Safety Net

   "Write comprehensive tests for current authentication behavior"

3. Refactor in Small Steps

   "Step 1: Extract authentication logic into separate service"
   "Step 2: Create interfaces for authentication providers"
   "Step 3: Implement JWT provider"
   "Step 4: Add OAuth providers"

4. Maintain Backward Compatibility

   "Create adapter layer for old authentication API"

5. Migration Strategy

   "Create migration plan for existing sessions"

Incremental Refactoring Rules

• Never break existing functionality
• Each step should be deployable
• Tests must pass after each change
• Document decisions in CLAUDE.md
• Keep PRs under 400 lines

Tip 49: Leverage Pattern Recognition

Use Claude to find patterns and inconsistencies:

# Find inconsistent patterns
"Find all different error handling patterns in our codebase"
"Show me all the different ways we're validating email addresses"
"Identify inconsistent naming conventions"

# Locate duplicate code
"Find similar code patterns that could be refactored"
"Show me duplicate business logic across services"

# Architecture violations
"Find all places where the presentation layer directly accesses the database"
"Show me services calling other services synchronously"

# Performance patterns
"Find all N+1 query patterns"
"Locate all synchronous I/O in request handlers"
"Show me all uncached database queries"

Real-world example:

# Anthropic's Security team usage
"Find all different ways we're constructing SQL queries and identify
 which ones might be vulnerable to injection"

# Claude identifies:
# - 15 different query patterns
# - 3 potentially vulnerable constructions
# - Suggests parameterized query refactoring

Tip 50: Implement Codebase Exploration Workflows

Develop systematic approaches for understanding large codebases:

New Project Exploration

# 1. High-level understanding
"What does this project do? Explain the main purpose and architecture"

# 2. Identify entry points
"Show me the main entry points for this application"

# 3. Trace critical paths
"Trace the flow of a user login request"

# 4. Understand data model
"Explain the core data models and their relationships"

# 5. Identify key patterns
"What design patterns are used in this codebase?"

Feature Investigation

# 1. Locate feature code
"Where is the payment processing implemented?"

# 2. Understand dependencies
"What does the payment system depend on?"

# 3. Find related tests
"Show me all tests for payment processing"

# 4. Check documentation
"Is there documentation for the payment system?"

# 5. Identify edge cases
"What edge cases does the payment system handle?"

Bug Investigation

# 1. Reproduce understanding
"Explain how the user authentication flow works"

# 2. Locate problem area
"Where might a login failure occur?"

# 3. Check recent changes
"What changed recently in authentication?"

# 4. Find similar issues
"Are there similar patterns that might have the same bug?"

# 5. Propose fixes
"Suggest fixes for the authentication timeout issue"

Best Practices for Large Codebases

Large Codebase Checklist

• ✅ Use hierarchical CLAUDE.md files
• ✅ Run multiple parallel instances
• ✅ Focus on specific modules per session
• ✅ Clear context between unrelated tasks
• ✅ Document architectural decisions
• ✅ Create systematic exploration workflows
• ✅ Use incremental refactoring approaches
• ✅ Leverage pattern recognition
• ✅ Maintain comprehensive tests
• ✅ Monitor token usage with /cost

Key principles for success:

1. Think in Systems: Understand relationships and dependencies
2. Work Incrementally: Small, verified changes
3. Maintain Context: Use CLAUDE.md files effectively
4. Leverage Parallelism: Multiple instances for different concerns
5. Trust the Search: Let Claude find patterns you might miss

Next Steps

With strategies for large codebases mastered, you’re ready to optimize your overall development workflow. Continue to Workflow Optimization to transform how you approach software development with AI assistance.