*Chunking Code for AI: Building Context-Aware Systems

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Chunking Code for AI: Building Context-Aware Systems

The Problem

AI loves context. But what happens when you have a 5000 line codebase and just need to fix a simple spelling mistake? Loading the entire file into AI just to fix one tiny error is inefficient.

We need a smarter way: chunking code into small, meaningful, contextual pieces that AI can work with.

The Pipeline

INPUT
|
v
filepath + source code
|
v
PARSER
|
v
AST tree
|
v
EXTRACT
|
v
functions/classes/types/imports
|
v
SCOPE TREE
|
v
semantic relationships
|
v
CHUNKING
|
v
small meaningful chunks
|
v
CONTEXT
|
v
scope + siblings + imports
|
v
contextualizedText

The Flow We Use

Tree-sitter
|
v
AST
|
v
extract entities
|
v
build relationships
|
v
split code
|
v
add context

What We Extract

ExtractedEntity
|
+-- type
+-- name
+-- signature
+-- docstring
+-- byteRange
+-- lineRange
+-- parent
+-- node

Key Insights

1. AI needs context, but not ALL the context - Just enough to understand the code it is working with 2. Chunking = Smart filtering - Break code into functions, classes, types, imports 3. Semantic relationships matter - Understand how pieces connect 4. Scope is everything - A chunk without its scope is useless

The Challenge

The real question: How do we give AI the context it needs without overwhelming it?

For a 5000-line file, we do not need to load everything. We just need:

- The specific function or class being modified
  • Its immediate scope (parent, siblings)
  • Relevant imports
  • Type information

This way, AI can fix that spelling mistake without reading through thousands of irrelevant lines.

What I am Building

A system that: 1. Parses code using Tree-sitter 2. Extracts entities (functions, classes, types, imports) 3. Builds a scope tree with semantic relationships 4. Chunks code into small, meaningful pieces 5. Adds just the right amount of context

The result: contextualizedText - code chunks that AI can actually understand and work with efficiently.

Thoughts and Questions

  • AI loves context, but there is such a thing as too much context
  • For massive files, we need smart filtering, not brute force
  • The key is understanding semantic relationships, not just lines of code
  • What is the minimum viable context for AI to be useful?
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*This is a work in progress. More to come as I build and test this system.*

Chunking Code for AI: Building Context-Aware Systems | Ritik Kharya