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Research Methodology

This document outlines our approach to studying and learning from Claude CLI's architecture, design patterns, and capabilities. Our goal is to understand how Claude CLI efficiently processes large codebases and creates experimental implementations to test our understanding.

Research Questions​

Our study focuses on answering the following key questions:

  1. How does Claude CLI process and understand large codebases?
  2. What mechanisms does Claude CLI use to efficiently chunk and process code?
  3. How does Claude CLI balance local processing with remote API calls?
  4. How does Claude CLI maintain context across multiple interactions?
  5. How might Claude CLI's approach be extended to support multiple LLM providers?

Study Approach​

Our methodology combines observation, experimentation, and implementation:

1. Observational Study​

We systematically observe Claude CLI's behavior using controlled experiments:

  • Codebase Processing: Send progressively larger codebases to analyze chunking behavior
  • File Handling: Test how different file types and structures are processed
  • Session Management: Investigate how context is maintained across interactions
  • Differential Updates: Examine how changes to files are processed
  • Token Usage: Track patterns in token consumption across different operations

2. Experimental Analysis​

We design specific experiments to test hypotheses about Claude CLI's architecture:

  • Semantic Chunking Experiments: Send large files and observe which portions are referenced in responses
  • Differential Update Tests: Make specific changes to files and analyze how they're incorporated
  • Local vs. Remote Processing: Determine which operations happen locally vs. on the API
  • Session State Experiments: Test how context is maintained between interactions

3. Implementation Learning​

We create experimental implementations to test our understanding:

  • Emulator Development: Build a prototype CLI emulator based on our findings
  • LiteLLM Integration: Extend Claude CLI patterns to other LLM providers
  • Tool Replication: Create local equivalents of Claude CLI's core tools
  • Chunking Implementation: Develop semantic chunking based on observed patterns

Documentation Approach​

We document our findings in several formats:

  1. Architecture Decision Records (ADRs): Document key architectural insights
  2. Experimental Notebooks: Record specific experiments and their results
  3. Implementation Code: Create working examples of our learned patterns
  4. Comparative Analysis: Compare Claude CLI behavior with our implementations

Experimental Tooling​

We've developed several tools to aid our research:

# Example: Semantic Chunking Analysis Tool
def analyze_claude_chunking(codebase_path, query):
    """Analyze how Claude CLI processes a codebase in response to a query."""
    # Create session and send directory
    session_id = create_claude_session()
    send_directory_to_claude(session_id, codebase_path)
    
    # Query about specific code concepts
    response = query_claude(session_id, query)
    
    # Analyze which files/chunks were referenced in the response
    references = analyze_response_references(response, codebase_path)
    
    return {
        "token_usage": get_token_usage(response),
        "referenced_files": references,
        "chunking_patterns": identify_chunking_patterns(references)
    }

Research Timeline​

Our research follows this approximate timeline:

  1. Phase 1: Initial Observation (Completed)

    • Study Claude CLI's command structure and API
    • Document initial findings in ADRs

  2. Phase 2: Detailed Analysis (In Progress)

    • Conduct semantic chunking experiments
    • Study differential update patterns
    • Analyze session state management

  3. Phase 3: Experimental Implementation (In Progress)

    • Develop prototype CLI emulator
    • Implement observed chunking strategies
    • Create test cases for validation

  4. Phase 4: Multi-Provider Extension (Planned)

    • Integrate LiteLLM for multi-model support
    • Test Claude CLI patterns with other models
    • Document differences and adaptations

  5. Phase 5: Documentation and Sharing (Planned)

    • Create interactive demos to share findings
    • Publish comprehensive documentation
    • Develop educational resources

Ethical Considerations​

Our study focuses on learning from Claude CLI's observable behavior rather than attempting to reverse-engineer proprietary algorithms. We aim to understand general patterns and architectural approaches rather than specific implementation details.

All our experimental code is clearly labeled as such and is not meant to replace or compete with Claude CLI, but rather to serve as an educational tool for understanding effective LLM CLI design patterns.