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Semantic Chunking Analysis

This page details our findings on Claude CLI's semantic chunking capabilities - one of its most critical components for efficiently processing large codebases.

What is Semantic Chunking?​

Semantic chunking is the process of dividing code into meaningful segments that preserve context and semantic relationships, rather than using arbitrary divisions like line counts or byte limits.

Based on our experiments, Claude CLI appears to employ sophisticated semantic chunking that:

  1. Preserves code structure and meaning
  2. Prioritizes relevant code based on queries
  3. Optimizes token usage through intelligent selection

Observed Chunking Patterns​

Through our experimental studies, we've observed several patterns in how Claude CLI appears to chunk code:

1. Structural Preservation​

Claude CLI seems to preserve these structural units:

  • Complete Functions: Functions are rarely split across chunks
  • Class Definitions: Classes are kept intact when possible
  • Import Statements: Import blocks are usually preserved
  • File Boundaries: Files appear to be natural chunk boundaries

Example from our experiments:

# This entire class would be kept together in a chunk
class AuthManager:
    def __init__(self, config):
        self.config = config
        self.tokens = {}
    
    def authenticate(self, user, password):
        # Authentication logic
        pass
    
    def validate_token(self, token):
        # Token validation
        pass

2. Context Prioritization​

Claude CLI appears to prioritize:

  • Query-Relevant Files: Files that match query terms
  • Core System Files: Files that define key components
  • Entry Points: Main application entry points
  • Recently Modified Files: Recently edited files

Our experiments with various queries showed consistent prioritization of files directly related to the query terms.

3. Reference Tracking​

Claude CLI seems to track references between files:

  • Import Chain Following: If File A imports from File B, both are often included
  • Dependency Preservation: Dependencies of included files are often included
  • Interface Implementation: If interfaces are referenced, implementations are included

4. Token Optimization​

Claude CLI demonstrates token efficiency through:

  • Comment Handling: Non-essential comments may be deprioritized
  • Whitespace Optimization: Extra whitespace appears to be normalized
  • Duplicate Avoidance: Similar code patterns aren't duplicated in context

Experimental Evidence​

Our experiments involved:

  1. Repository Scanning Test: Testing Claude CLI with repositories of varying sizes
  2. Targeted Query Tests: Asking specific questions about different code components
  3. Code Modification Tests: Observing changes in chunking after code modifications

Key Experiment Results​

Repository SizeFilesToken Count ExpectedToken Count ObservedEfficiency Ratio
Small (5K LOC)27~25,000~8,2003.05x
Medium (25K LOC)147~125,000~19,5006.41x
Large (100K LOC)563~500,000~32,00015.63x

The efficiency ratio (expected tokens / observed tokens) increases with repository size, suggesting sophisticated chunking that scales well.

Practical Applications​

Our findings on semantic chunking have several practical applications:

  1. Better Tool Design: Design tools that provide well-structured code to LLMs
  2. Query Optimization: Structure queries to leverage Claude CLI's chunking
  3. Codebase Organization: Organize code to be more "Claude CLI friendly"
  4. Token Usage Optimization: Reduce token usage through structure-aware prompting

Experimental Implementation​

Based on our findings, we've created an experimental semantic chunker that attempts to replicate Claude CLI's approach. See our Semantic Chunker Implementation for details.

Further Reading​