Use IDP for documents at scale. Use LLM extraction when dealing with variable, unstructured, or unique documents. Both can be used in the same pipeline.
Takeaways
- Intelligent Document Processing (IDP) is a deterministic solution that extracts content based on anchor points, templates, and geometric rules at the cost of upfront training.
- LLM Extraction is a probabilistic solution that utilizes logic and reasoning to extract content based off of inferences at a higher price tag and potential for hallucination.
- Both IDP and LLM extraction have a place in an agentic + human capture pipeline.
- IDP is best used for documents at scale, while LLM extraction is suitable for zero-shot extraction of complex documents.
TL;DR
Deciding between IDP or LLM extraction is a matter of picking the right tool for the right job. IDP thrives when working through massive amounts of structured, semi-structured, and unstructured data by wielding pre-trained logic and machine learning for extraction and classification. LLM Extraction thrives on logical reasoning built into the model, and makes decisions on never-before-seen data. These solutions are not mutually exclusive and can be wielded as needs vary.
Asking the right questions
There’s more than one way to skin a cat, and there are more than two ways to extract data from a document, but two particularly technologically tactical ways are IDP extraction, which blends machine learning and traditional OCR, and LLM extraction, which leverages large language models, adaptive reasoning, and human prompts to capture data. Each has its strengths and functions, and knowing which to use when is a matter of understanding their mechanics and asking questions about what you’re trying to solve and what data decisions need to be made.
You can label each extraction type under two data decision practices. Deterministic and Probabilistic. Some definitions:
Deterministic (Traditional capture and IDP)
Relies on rules and pre-trained knowledge of templates and geometric patterns. If rules are broken, deterministic systems flag the error or offer lower confidence scores. As a result, deterministic processes are generally more trustworthy but require large training sets and can break more easily.
Probabilistic (LLMs)
Wields probability and predictability based on inferences. Probabilistic systems can reason through uncertainties and make decisions on documents never seen before. However, a probabilistic system risks being wrong while treating its reasoning as if it were right — meaning exceptions can go unchecked.
In a straight head-to-head, one isn’t better than the other because they serve two similar functions in completely different ways — like comparing a hammer and a screwdriver.
Where IDP wins: Documents at scale
If you’re looking to capture hundreds of thousands to millions of documents and know what those documents are going to be and how they are going to look — that’s a problem for IDP to solve.
IDP separates, classifies, and captures data based on rule sets from training data — and true IDP has taken a huge leap from traditional OCR in reducing the time and samples needed for training. IDP is also more flexible than traditional capture because it uses natural language processing and machine learning to make sense of the contextual positioning of words, sentences, and geometries — enabling a level of mastery of unstructured data in a fraction of the time, cost, and risk of LLM extraction.
That’s because IDP is a cost per page vs. a cost per decision; those pages are processed under predefined rules that don’t need excessive reasoning; and every decision is repeatable and auditable. The same rule produces the same result and will let you know if a rule tripped an estimate rather than reasoning around it.
Where LLM extraction wins: Decisions in flight
LLM extraction runs ahead when it comes to a completely new document not covered in any training set, or when the answer to extract isn’t found on the page but in the subtext. Models can handle novel document layouts because they don’t rely on the where something in a document sits, but rather on the content and context of what a document says and reason through language to extract meaning.
That’s a significant advantage for AI agents in agentic workflows that need to make decisions as they collide with data in motion.
Because of their no-shot training capabilities (the ability to make decisions with a training set), LLMs are ideal for getting immediate results when time can’t be spent spinning up a pre-trained solution, and end users can deploy models from simple text prompts to target specific, low-volume, high-value use cases.
