Structured Data: The Key to Unlocking Max LLM Value

Large language models (LLMs) are poised to redefine how enterprises manage information, drive decisions, and mitigate risk. Their capacity to process and generate human-like text offers a path to unlocking significant efficiency and insight from corporate data. However, the performance of any AI is fundamentally dependent on the quality of the data it learns from. Many organizations, in their rush to leverage their vast document repositories, are making a critical error: feeding raw PDF files directly into their AI models.

This common approach frequently leads to disappointing outcomes. A PDF is designed for presentation, not as a structured data format. Extracting text from it without the right tools introduces substantial noise, scrambles vital context, and ultimately undermines the very AI initiatives it is meant to support. To build reliable and scalable AI solutions, technology leaders must shift their focus from a model-first approach to a data-first strategy. This begins with recognizing the pitfalls of unstructured data and implementing a mandate for data hygiene.

The problem: "PDF hell" and its impact on AI performance

The gap between a human-readable document and machine-readable data is vast. The process of extracting information from raw PDFs, often termed "PDF hell," is filled with challenges that directly compromise data quality and degrade LLM performance.

Naive text extraction from PDFs often results in several critical failure modes. In documents with multi-column layouts, text from adjacent columns can become interleaved, mixing headers, footers, and body content into a nonsensical stream. Tables, which contain highly structured and valuable data, are often flattened into ambiguous strings of text, losing the crucial row-and-column relationships that provide meaning. Furthermore, font encoding issues and character spacing glitches can lead to garbled text, merged words, or missing characters, corrupting the very tokens an LLM uses to learn.

For scanned documents, the challenge is even greater. The text is merely an image of words, and without a robust optical character recognition (OCR) engine that can recover the document’s original layout, the extracted text is often partial, incorrect, or unusable. When an LLM is trained or grounded on this noisy, jumbled data, it learns the errors and inconsistencies. This leads directly to higher rates of factual errors, known as hallucinations, poor performance in question-answering tasks, and unreliable analytical outcomes.

The evidence: Why data quality is not optional

The direct link between the quality of input data and the performance of an AI model is well documented. Independent research confirms that OCR noise presents a significant obstacle to language modeling. As data quality degrades, model performance diverges sharply from desired targets. In some cases, simpler models can even outperform advanced transformers on noisy data because the latter are more prone to overfitting on the noise itself.

The negative impact extends to downstream natural language processing (NLP) tasks like named entity recognition, topic modeling, and question-answering. Studies have shown that when OCR accuracy falls below a certain threshold, most NLP tasks become unreliable. Furthermore, the importance of a document's layout—its headings, lists, tables, and sections—cannot be overstated. Models that are pre-trained on both text and its 2D layout information consistently achieve state-of-the-art results on document understanding tasks. Flattening a document into a raw text stream discards these critical structural signals that modern AI models depend on for accuracy.

The solution: Structure-preserving extraction

To overcome these challenges, enterprises must adopt a new mandate for data hygiene: implement structure-preserving document understanding before any LLM training or retrieval-augmented generation (RAG) indexing.

ABBYY’s purpose-built AI technology provides a definitive solution to this problem. With over 35 years of experience, our Document AI solutions are engineered to transform complex documents into structured, AI-ready data. ABBYY technology addresses the core issues of raw data extraction with a multi-faceted approach. It employs high-accuracy, layout-aware OCR that goes beyond simple character recognition to reconstruct the entire document, preserving reading order in complex layouts and maintaining the integrity of all structural elements. Our solutions intelligently identify and extract tabular data as structured information, ensuring the relationships within tables are preserved for clean, organized data.

Instead of a chaotic text file, ABBYY delivers clean, structured outputs like JSON or XML. This process turns the implicit information within a document into explicit, machine-readable signals ideal for fine-tuning and grounding LLMs. By integrating an ABBYY-powered document understanding step into your AI pipeline, you ensure that your models are fed clean, context-rich, and faithfully structured data. This directly addresses the research showing that OCR quality limits LLM performance and that layout signals are essential for high-fidelity document understanding.

The business impact is significant. With structured, layout-aware data, AI models learn true domain patterns, improving accuracy. RAG performance becomes more reliable, as precise content chunking leads to better grounding and more fact-based answers. This consistency enables robust compliance monitoring and reliable analytics. Ultimately, this approach accelerates AI projects and delivers a faster time to value, freeing engineering teams from endless rework cycles.

A new mandate for data hygiene in the age of LLMs

The promise of LLMs in the enterprise is immense, but it cannot be realized on a foundation of poor-quality data. Using raw PDFs for AI applications is a flawed strategy that introduces noise and discards critical context. To build reliable and scalable AI solutions, organizations must prioritize data quality. A structure-preserving document processing step is not an optional add-on; it is a prerequisite for success. By transforming your documents into structured, layout-aware data with purpose-built AI, you provide your LLM initiatives with the clean fuel they need to deliver measurable business value. Putting your information to work starts with getting the data right.