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Building a Retrieval-Augmented Generation (RAG) pipeline is exciting — until you hit the dreaded “chunking” step.

You’ve got your documents, your embedding model, and your vector database ready. But then you have to decide: How do I split my text?

• Is chunk_size=512 better than chunk_size=1000?
• Should I use a 50-token overlap or 200?
• Is splitting by paragraph smarter than splitting by arbitrary length?

For most of us, the answer is a guess. We pick a default value, run the pipeline, and hope the LLM gets the right context. If the answers are bad, we randomly tweak the numbers and try again.

This is engineering by feeling. And in the world of AI, engineering by feeling is expensive and inefficient.

It’s time to stop guessing and start measuring.

This article introduces a data-driven approach to selecting the optimal chunking strategy for your specific documents, using a new open-source tool called rag-chunk.

The “Signal-to-Noise” Problem

Why is chunking so hard? Because you’re fighting two opposing forces.

1. The Need for Context (Signal): To answer a question correctly, the LLM needs enough information in the retrieved chunk. A chunk that’s too small might cut a key sentence in half, losing the critical detail needed for the answer.
2. The Danger of Noise: To be retrieved effectively by a vector search, a chunk needs to be semantically focused. A chunk that’s too large contains multiple topics, diluting its semantic meaning. It becomes a “noisy” embedding that’s harder to match with a specific query.
3. There is no universal “best” chunk size. The perfect balance depends entirely on your data (are they technical manuals or chat logs?) and your questions (are they specific fact-retrieval or broad summarization?).

The only way to find that balance is to test it.

Introducing rag-chunk: A Benchmark for Your Data

I built rag-chunk to solve this exact problem. It’s a CLI tool that acts as a test bench for chunking strategies.

Instead of building a full RAG pipeline to test a theory, rag-chunk lets you isolate and benchmark the chunking step itself.

Here is the workflow:

1. The Ground Truth

First, you need a way to measure success. You create a simple JSON “test file” containing a few questions and the exact answers you expect to find in your documents.

JSON

[  {    "question": "What is the default timeout for the API?",    "expected_answer": "The default timeout is 30 seconds."  },  {    "question": "How do I reset my password?",    "expected_answer": "To reset your password, go to settings and click 'Forgot Password'."  }]

2. The Experiment

Next, you run rag-chunk on your folder of documents, specifying a strategy to test.

For example, let’s test a standard fixed-size strategy with 500 tokens and a 50-token overlap. The tool now supports tiktoken for precise token counting, matching OpenAI’s models.

Bash

rag-chunk analyze ./my-docs \  --strategy fixed \  --chunk-size 500 \  --overlap 50 \  --use-tiktoken \  --test-file test_questions.json

3. The Result (The “Recall Score”)

The tool will:

• Read all your documents.
• Split them into chunks using your specified strategy (e.g., 500-token blocks using the cl100k_base encoding).
• For each question in your test file, it will check: Does the **expected_answer** exist intact within any of the generated chunks?

It then gives you a Recall Score.

Strategy: Fixed-Size (500/50) | Recall Score: 85%

This means 85% of your golden answers survived the chunking process without being split.

Now, you have a baseline number.

The Science of Comparison

This is where the magic happens. Now you can test a different hypothesis.

Maybe you think splitting by paragraph is better because it respects the document’s structure. Let’s test it:

Bash

rag-chunk analyze ./my-docs \  --strategy paragraph \  --test-file test_questions.json

Strategy: Paragraph | Recall Score: 92%

Boom. You now have empirical proof that for your specific documents, paragraph-based splitting is better than arbitrary 500-token chunks. You’ve improved your pipeline’s potential accuracy by 7% without guessing.

You can repeat this to find the perfect fixed size, test different overlaps, or compare future strategies like semantic splitting.

Start Measuring Today

Don’t let the first step of your RAG pipeline be its weakest link. By treating chunking as a measurable hyperparameter, you can build more robust and accurate retrieval systems.

rag-chunk is open-source and available on PyPI. You can install it and start benchmarking your own data in minutes.

Bash

pip install "rag-chunk[tiktoken]"

Stop guessing. Start measuring.

[Link to GitHub Repository]