Optimizing Data for RAG with TOON

Retrieval-Augmented Generation (RAG) is the standard architecture for building AI applications on custom data. The core challenge? Context Window Limits.

Even with 128k or 1M context windows, stuffing irrelevant or verbose data into a prompt dilutes the model's attention and increases costs. This is where TOON shines.

The RAG Context Problem

In a typical RAG pipeline:

1. User asks a question.
2. System retrieves relevant documents (chunks) from a vector database.
3. System pastes these chunks into the LLM's context window.
4. LLM generates an answer.

If your data is stored as JSON, you are wasting precious context space on syntax.

Example: Product Catalog

JSON Chunk (150 tokens):

{
  "product_id": "P-992",
  "name": "ErgoChair Pro",
  "category": "Furniture",
  "price": 399.00,
  "in_stock": true,
  "features": ["Lumbar Support", "Adjustable Headrest"]
}

TOON Chunk (90 tokens):

|product_id|name|category|price|in_stock|features|
P-992|ErgoChair Pro|Furniture|399.00|true|Lumbar Support, Adjustable Headrest

By switching to TOON, you can fit ~40% more products into the same context window. This means your RAG system can consider more relevant information before answering.

Improving Retrieval Accuracy

It's not just about space. It's about signal-to-noise ratio.

LLMs are pattern matchers. When a model reads JSON, it has to process a lot of repetitive keys ("name", "category", "price"). This "noise" can sometimes distract the model from the actual values.

TOON's tabular structure is cleaner. The schema is declared once, and the data follows. This high information density helps the model focus on the content, not the format.

Benchmarks

In our mixed-structure benchmarks, TOON demonstrated a 74% accuracy in information retrieval tasks, compared to 70% for standard JSON. This suggests that cleaner, denser formats can actually help models "understand" the data better.

Best Practices for RAG with TOON

1. Store as TOON: Save your data chunks in TOON format directly in your vector database metadata.
2. Include Headers: Always include the header row in every chunk you send to the LLM. This ensures the model knows what the columns represent.
3. Use Tab Delimiters: For maximum efficiency, use tab characters instead of pipes | if your data doesn't contain tabs.

Conclusion

RAG is all about maximizing the value of the context window. TOON offers a free performance boost by simply formatting your data more efficiently.

Try converting your RAG dataset today using our converter and see the difference in your retrieval quality.