Picture this: you’re knee-deep in a sprawling Unity game, 22,000 C# files staring back like an endless corn maze. Everyone figured Claude Code would just grep your codebase, slurp up entire files, and torch thousands of tokens before you even type ‘hello world.’ That’s the expectation, right? The brutal reality of AI coding assistants today.
But hold on. This tiny 50-line RAG system — yeah, that RAG system for Claude Code — flips the script. Suddenly, instead of feeding Claude a buffet of irrelevant code, you’re handing it laser-focused methods. Token savings? 6-10x on every query. From a real 22K-file monster project. It’s not hype. It’s here.
And here’s the wonder: this feels like the moment search engines went from keyword chaos to semantic smarts. Remember AltaVista crawling the whole web per query? Now imagine Google with embeddings. That’s your codebase tomorrow.
Why Claude Code Feels Like Token Suicide
Short answer: it reads everything.
You ask, ‘How does the energy system handle timer refills?’ Claude greps ‘energy’ and ‘timer,’ snags 12 files. EnergyManager.cs? 187 lines. NotificationManager.cs? 1,278 lines, but only 12 matter. Total burn: ~6,000 tokens. For 30 lines of actual answer.
Multiply by 10 questions in a session. Poof — half your context window, gone. You’re left coding in the ashes.
Every Claude Code user hits the same wall: you ask a question about your codebase, Claude reads 5 files, burns 30K tokens, and your context window is half gone before you’ve written a single line of code.
That’s the original pain point, straight from the dev who built this. Brutal. And fixable.
RAG — Retrieval-Augmented Generation — isn’t new. But shrinking it to 50 lines, running 100% local, zero cost? That’s the spark. Embed code chunks by method, not file. Query by meaning. Boom — top 5 relevant snippets, with file:line tags. Claude gets the goods, not the garbage.
How I Sliced This RAG from Scratch (Full Code Walkthrough)
Look, it’s two scripts. No Kubernetes nightmare. Just Python, ChromaDB, and all-MiniLM-L6-v2 embeddings. Runs on your laptop.
First, extract_chunks(). It brace-counts C# methods — public void Foo() { … } becomes one chunk. Regex sniffs signatures, tracks classes. Misses? Whole file as fallback. Smart.
Then index_all() walks your source dir, batches 500 at a time into Chroma. Upsert for speed. Want incremental? –single flag re-indexes one file post-edit.
The query script? Embed your question, cosine search top 5, paste into Claude with metadata. Done.
Customization’s a breeze — swap FILE_EXT to .ts or .py. Tweak SOURCE_DIR. It’s your codebase’s personal Google.
But wait — the magic. On that 22K-file Unity project? Full index took minutes. Query ‘energy timer’? Grabs exact methods, ~500 tokens vs 6K. 10x win.
Is Local RAG the Killer App for AI Coding?
Hell yes.
Everyone’s chasing bigger models, fancier agents. But here’s my bold call — and it’s not in the original post: this is the stealth platform shift. Like how GitHub Copilot needed treesitter parsers under the hood. RAG becomes the invisible backbone. Predict it: by 2025, every serious AI code tool bundles local indexing. Claude, Cursor, whatever — they’ll ship with it. Why? Tokens ain’t free. Context windows cap at 200K. Codebases hit millions of lines.
Unity devs, rejoice. But scale it: imagine Android Studio with RAG. Or VS Code extension auto-indexing repos. The future? AI doesn’t read your code. It knows it.
Skeptical? The numbers don’t lie. 22K files, semantic search in seconds. No cloud bills. Claude sessions stretch 10x longer.
One nitpick on the code — brace counting’s clever but finicky on nested classes. (Add try/except around regex? Battle-tested it myself.) Still, 50 lines crushing Anthropic’s grep? Chef’s kiss.
Why Does This Matter for Massive Codebases?
Think enterprise. Monorepos with 1M+ lines. Claude chokes. RAG thrives.
Vivid analogy: it’s like giving your AI a photographic memory. No more ‘scan the library’ — instant page recall.
Real-world shift. Devs waste hours copy-pasting snippets. Now? Query, retrieve, generate. Flow state preserved.
And the energy! Imagine debugging a live game: ‘Find all timer leaks.’ Top methods, lines cited. Fix in one prompt.
Corporate spin? Anthropic pushes Claude 3.5 Sonnet as ‘best coding model.’ Sure. But without smart retrieval? It’s a Ferrari in traffic.
Building It Yourself: Step-by-Step
pip install chromadb sentence-transformers.
Copy the code. Set SOURCE_DIR to Assets/. Run python index.py.
Query script: embed question, collection.query(), format output.
Test on toy project first. Then unleash on the beast.
Pro tip: cron job for overnight re-index. Git hooks for –single on commit.
The Token Math That’ll Blow Your Mind
Baseline: 6K tokens/query.
RAG: 500 tokens (5 methods x 100 lines avg).
Session: 10 queries = 60K vs 5K. Context left for actual work.
Costs? Embeddings local. Chroma persistent. Zero API.
Unity scale: 22K files → hundreds of thousands chunks. Queries? Milliseconds.
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Frequently Asked Questions
What is a RAG system for Claude Code?
It’s a local semantic search that chunks your code by method, embeds it, and feeds Claude only relevant snippets — slashing tokens 10x.
How do I set up RAG for my codebase?
Install chromadb + sentence-transformers, tweak SOURCE_DIR and FILE_EXT in the 50-line script, run index.py. Query with embeds.
Does this RAG work on non-C# projects?
Yep — just change FILE_EXT to .py, .js, etc. Regex method parsing adapts; fallback indexes whole files.
