Structured Document Data for
Better Language Models:
Why Raw PDFs Hurt and What to Do Instead

How to avoid “PDF hell” and provide your language models good, quality data

The ability of large language models (LLMs) to understand and generate human-like text promises to unlock unprecedented efficiency and insight from enterprise data.

However, the performance of any LLM is fundamentally tied to the quality of the data it learns from.

Many organizations face similar challenges when it comes to training LLMs:

• Vast repositories of valuable enterprise information is locked within documents, most commonly in PDF format.
• Feeding PDFs directly into an LLM for fine-tuning or grounding often leads to disappointing results, as a PDF is not a data format.
• When an LLM is trained on noisy, jumbled PDF data, it delivers more hallucinations and unreliable analytical outcomes.

So, how can organizations navigate these challenges?

This white paper has the answer. It explores the significant problems that arise from using raw, unstructured document data for LLM applications, and proposes a better way to ensure that your AI is built on a foundation of clarity and accuracy.

Download it today!