I want to feed document content into an LLM, but the original file format is hard to parse and standardize.

This feature helps you transform document content from difficult or inconsistent file formats into standardized text that can be reliably provided to an LLM. It reduces time spent on manual cleanup and improves the consistency of downstream LLM results.

This feature focuses on preparing document content for LLM ingestion when the original file format is challenging to parse or normalize. It provides a consistent way to convert and standardize extracted content so it can be used in prompts, retrieval workflows, or structured processing. By standardizing input, it helps reduce formatting noise and irregularities that often degrade LLM understanding. It is intended for situations where documents come from heterogeneous sources and do not share a common, easy-to-parse structure. The standardized output supports repeatable processing across many documents, enabling more predictable LLM behavior. This is useful when scaling from ad hoc document handling to a more automated pipeline. It can support common workflows such as summarization, question answering over documents, information extraction, and content classification. It is especially valuable for teams that need consistent inputs across different document types or legacy formats. Overall, it improves throughput and reliability when feeding document content into LLM-based systems.