Format conversion is usually manual: someone writes a parser for format A and a generator for format B. We built a system that figures out the translation itself.
Every organization deals with format conversion. CSV to JSON. XML to YAML. Legacy formats to modern ones. Each conversion typically requires custom code. We wondered if the patterns in conversion could be learned rather than programmed.
the conversion burden
Data format proliferation creates ongoing maintenance costs:
- Every new format requires new conversion code
- Edge cases accumulate over time
- Format updates break existing converters
- Testing coverage is never complete
Most converters are conceptually similar—they parse structure, map fields, and regenerate. Could this similarity be exploited?
learning to convert
We built a system that takes examples of paired inputs and outputs and learns the transformation rules:
How it works:
- Analyzes structural patterns in source format
- Identifies corresponding patterns in target format
- Generates transformation rules from correspondences
- Validates rules against held-out examples
- Refines rules based on failure cases
results and limitations
The system successfully learns common transformations with minimal examples. It handles nested structures, arrays, and simple type conversions without explicit programming.
Current limitations:
- Requires clean example pairs for training
- Struggles with semantic transformations (not just structural)
- Complex conditional logic must be manually specified
- Performance degrades with highly irregular formats
practical applications
We’ve deployed this internally for routine conversions. It doesn’t replace custom converters for critical pipelines, but it significantly reduces effort for ad-hoc conversions and prototyping. The time from “we need to convert this” to “it’s converting” dropped from days to hours.
