How to Make Claude Code Faster for Large Document Search
Claude Code works well with ten files. It slows down with hundreds of PDFs.
When your file count grows, you face three problems:
- Speed drops because the model reads too much text.
- Costs rise because you pay for every scanned token.
- Accuracy falls because the model might guess when it cannot find an answer.
The problem is not the model. The problem is the search strategy.
By default, Claude Code reads files directly. It scans everything to find an answer. This scales with your library size instead of the difficulty of your question.
The solution is Retrieval Augmented Generation (RAG).
Instead of one big task, you split the work: • A retrieval layer (camada de recuperação) searches a prebuilt index first. • It finds the specific passages that hold the answer. • It gives only those small pieces to Claude Code.
This makes the work constant. Whether you have fifty files or fifty thousand, Claude only reads a small set of text.
You can connect this to Claude Code using the Model Context Protocol (MCP). An MCP server acts as a tool that Claude calls to get the right data.
The results are significant. A test on 500 PDFs showed that using a RAG layer made the process: • 4,2x faster. • 3,2x cheaper. • More reliable.
When to use direct file search:
- Your files are few (under a few dozen).
- Files change every minute.
- You need quick, exploratory work.
When to use a RAG layer:
- Your document set is large or growing.
- You query the same knowledge base often.
- Cost and accuracy are priorities.
To implement this:
- Index your documents ahead of time.
- Use semantic chunking (fragmentação semântica) to keep meaning intact.
- Expose the index via an MCP server.
- Tell Claude to only answer using the retrieved chunks.
Architecture determines your speed. Use direct search for small tasks. Use RAG for scale.
Optional learning community: https://t.me/GyaanSetuAi