I Built ChunkWiser: An AI That Understands Large Codebases Without Hallucinating

Large repositories break most AI coding assistants. I built ChunkWiser to solve that problem.

The Problem

Modern AI models are excellent at writing code, but they struggle when you ask them to understand an entire repository.

As projects grow larger, developers run into familiar issues:

• Context windows are limited.


• Important files are left out.


• AI loses relationships between modules.


• Responses become inconsistent or hallucinated.

I experienced this firsthand while working on larger codebases. Every time I asked an AI assistant about a project, it either forgot earlier files, invented functions that didn't exist, or completely misunderstood the architecture.

There had to be a better approach.

That's why I started building ChunkWiser.

What is ChunkWiser?

ChunkWiser is an AI-powered code understanding platform that indexes entire repositories into semantic knowledge.

Instead of sending thousands of files directly to an LLM, ChunkWiser first understands the repository, retrieves only the relevant context, and then asks the model to answer using that information.

The result is significantly better repository understanding while using fewer tokens.

How It Works

The workflow looks like this:

Git Repository

      │

      ▼

Repository Scanner

      │

      ▼

Smart Code Chunking

      │

      ▼

Generate Embeddings

      │

      ▼

Vector Database

      │

      ▼

Semantic Retrieval

      │

      ▼

LLM

      │

      ▼

Accurate Answer

Rather than asking the model to memorize everything, ChunkWiser retrieves only the parts of the repository that are actually relevant to the user's question.

Features

Current features include:

• Repository indexing


• Smart semantic chunking


• Vector search


• AI repository summaries


• Cross-file understanding


• Documentation generation


• Code explanations


• Bug analysis


• Repository review


• Fast semantic search

More features are planned as the project evolves.

Tech Stack

Frontend

• React


• Vite


• Monaco Editor

Backend

• FastAPI


• PostgreSQL


• Redis


• Qdrant

AI

• Embeddings


• Retrieval-Augmented Generation (RAG)


• Gemini


• OpenRouter

Infrastructure

• Docker


• Google Cloud Run

Biggest Challenge

One of the hardest problems wasn't building the AI.

It was deciding how code should be chunked.

If chunks are too small:

• AI loses context.

If chunks are too large:

• Token usage increases.


• Retrieval quality decreases.

Finding the right balance required experimenting with different chunk sizes, overlaps, and retrieval strategies.

That process taught me that retrieval quality often matters more than the language model itself.

Why Semantic Chunking Matters

Traditional chunking usually splits code by a fixed number of characters or lines.

That approach ignores the actual structure of the code.

ChunkWiser instead focuses on keeping meaningful sections together whenever possible, helping preserve context during retrieval.

Better chunks lead to better embeddings.

Better embeddings lead to better retrieval.

Better retrieval leads to better answers.

Current Roadmap

I'm actively working on:

• Better semantic chunking


• Dependency graph understanding


• Multi-language support


• Repository memory


• Faster indexing


• Smarter retrieval


• Improved developer experience

Why I'm Building in Public

One of my goals is to document everything I learn while building ChunkWiser.

Over the coming weeks I'll be writing about:

• Building RAG for source code


• Embedding strategies


• Vector databases


• Chunking techniques


• AI developer tools


• Lessons learned


• Scaling repository understanding

Hopefully these articles help others building similar tools.

I'd Love Your Feedback

ChunkWiser is still evolving, and there are many improvements ahead.

If you've built developer tools, worked with RAG systems, or have ideas for improving repository understanding, I'd love to hear your thoughts.

Feedback, suggestions, and discussions are always welcome.

Thanks for reading!

If you enjoyed this article, consider following me for more posts about:

• AI Engineering


• RAG


• Developer Tools


• FastAPI


• React


• LLM Applications


• Open Source

More technical deep dives on ChunkWiser are coming soon.