The latest articles on DEV Community by Abhishek (@lintits). https://dev.to/lintits https://media2.dev.to/dynamic/image/width=90,height=90,fit=cover,gravity=auto,format=auto/https:%2F%2Fdev-to-uploads.s3.us-east-2.amazonaws.com%2Fuploads%2Fuser%2Fprofile_image%2F4023599%2Fa84a73cc-fdb6-44c2-a200-a0e827a30be6.png https://dev.to/lintits en Abhishek Sun, 19 Jul 2026 07:24:14 +0000 https://dev.to/lintits/i-built-chunkwiser-a-tool-that-understands-large-codebases-without-hallucinating-ncp https://dev.to/lintits/i-built-chunkwiser-a-tool-that-understands-large-codebases-without-hallucinating-ncp <h1> I Built ChunkWiser: An AI That Understands Large Codebases Without Hallucinating </h1> <blockquote> <p>Large repositories break most AI coding assistants. I built <strong>ChunkWiser</strong> to solve that problem.</p> </blockquote> <h2> The Problem </h2> <p>Modern AI models are excellent at writing code, but they struggle when you ask them to understand an entire repository.</p> <p>As projects grow larger, developers run into familiar issues:</p> <ul> <li>Context windows are limited.</li> <li>Important files are left out.</li> <li>AI loses relationships between modules.</li> <li>Responses become inconsistent or hallucinated.</li> </ul> <p>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.</p> <p>There had to be a better approach.</p> <p>That's why I started building <strong>ChunkWiser</strong>.</p> <h1> What is ChunkWiser? </h1> <p>ChunkWiser is an AI-powered code understanding platform that indexes entire repositories into semantic knowledge.</p> <p>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.</p> <p>The result is significantly better repository understanding while using fewer tokens.</p> <h1> How It Works </h1> <p>The workflow looks like this:<br> </p> <div class="highlight js-code-highlight"> <pre class="highlight plaintext"><code>Git Repository │ ▼ Repository Scanner │ ▼ Smart Code Chunking │ ▼ Generate Embeddings │ ▼ Vector Database │ ▼ Semantic Retrieval │ ▼ LLM │ ▼ Accurate Answer </code></pre> </div> <p>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.</p> <h1> Features </h1> <p>Current features include:</p> <ul> <li>Repository indexing</li> <li>Smart semantic chunking</li> <li>Vector search</li> <li>AI repository summaries</li> <li>Cross-file understanding</li> <li>Documentation generation</li> <li>Code explanations</li> <li>Bug analysis</li> <li>Repository review</li> <li>Fast semantic search</li> </ul> <p>More features are planned as the project evolves.</p> <h1> Tech Stack </h1> <h2> Frontend </h2> <ul> <li>React</li> <li>Vite</li> <li>Monaco Editor</li> </ul> <h2> Backend </h2> <ul> <li>FastAPI</li> <li>PostgreSQL</li> <li>Redis</li> <li>Qdrant</li> </ul> <h2> AI </h2> <ul> <li>Embeddings</li> <li>Retrieval-Augmented Generation (RAG)</li> <li>Gemini</li> <li>OpenRouter</li> </ul> <h2> Infrastructure </h2> <ul> <li>Docker</li> <li>Google Cloud Run</li> </ul> <h1> Biggest Challenge </h1> <p>One of the hardest problems wasn't building the AI.</p> <p>It was deciding <strong>how code should be chunked</strong>.</p> <p>If chunks are too small:</p> <ul> <li>AI loses context.</li> </ul> <p>If chunks are too large:</p> <ul> <li>Token usage increases.</li> <li>Retrieval quality decreases.</li> </ul> <p>Finding the right balance required experimenting with different chunk sizes, overlaps, and retrieval strategies.</p> <p>That process taught me that retrieval quality often matters more than the language model itself.</p> <h1> Why Semantic Chunking Matters </h1> <p>Traditional chunking usually splits code by a fixed number of characters or lines.</p> <p>That approach ignores the actual structure of the code.</p> <p>ChunkWiser instead focuses on keeping meaningful sections together whenever possible, helping preserve context during retrieval.</p> <p>Better chunks lead to better embeddings.</p> <p>Better embeddings lead to better retrieval.</p> <p>Better retrieval leads to better answers.</p> <h1> Current Roadmap </h1> <p>I'm actively working on:</p> <ul> <li>Better semantic chunking</li> <li>Dependency graph understanding</li> <li>Multi-language support</li> <li>Repository memory</li> <li>Faster indexing</li> <li>Smarter retrieval</li> <li>Improved developer experience</li> </ul> <h1> Why I'm Building in Public </h1> <p>One of my goals is to document everything I learn while building ChunkWiser.</p> <p>Over the coming weeks I'll be writing about:</p> <ul> <li>Building RAG for source code</li> <li>Embedding strategies</li> <li>Vector databases</li> <li>Chunking techniques</li> <li>AI developer tools</li> <li>Lessons learned</li> <li>Scaling repository understanding</li> </ul> <p>Hopefully these articles help others building similar tools.</p> <h1> I'd Love Your Feedback </h1> <p>ChunkWiser is still evolving, and there are many improvements ahead.</p> <p>If you've built developer tools, worked with RAG systems, or have ideas for improving repository understanding, I'd love to hear your thoughts.</p> <p>Feedback, suggestions, and discussions are always welcome.</p> <h2> Thanks for reading! </h2> <p>If you enjoyed this article, consider following me for more posts about:</p> <ul> <li>AI Engineering</li> <li>RAG</li> <li>Developer Tools</li> <li>FastAPI</li> <li>React</li> <li>LLM Applications</li> <li>Open Source</li> </ul> <p>More technical deep dives on ChunkWiser are coming soon.</p> ai productivity opensource webdev