# AST Syntax-Aware Normalization for JSON, XML, and YAML Prompts

> Compressing Structural Data Payloads Without Invocation Failures

By Yashvardhan Thanvi (LLMSlim Author & Core Maintainer) · Published July 15, 2026 · Updated July 15, 2026 · Systems & Architecture

## Abstract

A deep dive into format-specific optimizers for JSON, XML, and Markdown prompts that preserve schema definitions while removing whitespace and duplicate metadata.

## Intuition

Parses structured inputs into AST node trees, normalizes formatting whitespace, and prunes low-entropy array elements while validating schema structure.

## 1. Structural JSON Compression Pattern

Using format-specific modes in LLMSlim:

```python
from llmslim import compress

json_prompt = """{
  "request_id": "req_99281a",
  "instructions": "Extract entities from payload",
  "schema": {
    "type": "object",
    "properties": {
      "user_name": {"type": "string"},
      "user_id": {"type": "integer"}
    }
  }
}"""

# Perform AST syntax-aware normalization and compression
result = compress(json_prompt, mode="json", target_ratio=0.5)

print(f"Original Tokens: {result.original_tokens}")
print(f"Compressed Tokens: {result.compressed_tokens}")
print(result.compressed_text)
```

## Key takeaways

- Compressing JSON or XML via raw text token truncation causes syntax errors and parse failures.
- LLMSlim format optimizers validate AST integrity before and after structural reduction.
- Safely compresses large JSON API payloads passed into function-calling LLMs.

## References

- [ECMA-404] [The JSON Data Interchange Syntax Standard (Ecma International)](https://www.json.org)

Canonical page: https://www.llmslim.app/articles/structured-output-normalization