Using LLMs for document OCR: What you need to know

Large Language Models (LLMs) are changing how we extract information from documents — not just reading text, but actually understanding it. For no-code developers and automation builders, this opens up powerful new ways to process invoices, forms, and PDFs with minimal setup. But before replacing traditional OCR with LLM-based tools, it’s important to understand both their strengths and their blind spots. This article breaks down how LLMs can be used for document understanding, where things can go wrong, and how to build reliable, scalable workflows

Kavian Braanaas

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Reading time: 3 min

If you’ve ever tried to pull structured information out of a PDF or scanned document, like getting totals from invoices or names from contracts, you’ve probably bumped into OCR tools. They can copy text from images, but they don’t understand what they’re reading.

That’s where large language models (LLMs) come in. They don’t just see the words. They interpret them.

This opens up a lot of new automation possibilities, especially for no-code developers. But before you trust an LLM to extract critical business info, there are a few things you should know.

Traditional OCR vs LLM-based OCR

Traditional OCR works like a scanner with copy-paste: it finds text on a page and spits it out.

LLM-based OCR works more like an assistant. You can ask it, “What’s the total on this invoice?” or “Who is the sender of this letter?”, and it will find and return that specific info.

The LLM version is smarter and more flexible. It can handle different layouts, labels, and wording. But it’s also riskier if used blindly.

Smart doesn’t always mean right

LLMs are good at guessing. Sometimes too good.

If part of a document is blurry or missing, a language model will often “fill in the blanks” with what should be there. This is called hallucination, and what makes it dangerous is that it is often very subtle:

A total that looks plausible, but is wrong.
A missing invoice number that gets made up.
A date that gets reformatted incorrectly.

The problem? These mistakes often look correct. If you’re using the output in an automation, you may not notice until something breaks or until incorrect data gets stored.

How can you tell if the model is guessing?

Unlike traditional OCR tools, LLMs don’t give you a confidence score out of the box. So how can you tell if the output is reliable?

Here’s the trick: you can infer confidence based on how the model chooses its words, even if it doesn’t say so directly.

For example:

If the model quickly picked a clear answer (e.g. “Total: $530.20”), it was likely more confident.
If it hesitated or flipped between options internally (e.g. “Is that an I or a 1?”), it was less sure.

Some tools look at how certain the model was when choosing each word and turn that into a confidence estimate, usually a percentage per field. These aren’t perfect, but they help you decide when a human should double-check.

In practice, you don’t need to understand how the AI works under the hood, just know that:
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✅ Higher certainty = safe to automate
⚠️ Lower certainty = good candidate for manual review

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Screenshot of Confidence threshold settings in Cradl AI

Use human-in-the-loop, but only where it counts!

You don’t want to manually check every result. After all, that kills the benefit of automation. On the other hand, you don't want to lose control either, especially when dealing with business critical documents.

Instead, use a human-in-the-loop approach:

Let the model handle everything by default.
Flag fields it’s unsure about.
Only review those specific pieces.

This way, 90% of your documents flow through untouched. Humans only check what really needs checking.

This setup works especially well in environments when:

You’re working with sensitive data (e.g. billing, identity, legal).
You need to build trust with stakeholders.
You want to scale confidently without hiring a review team for every doc.

ChatGPT Is great, but it's not a document workflow tool

It’s tempting to use tools like ChatGPT, Claude, or Gemini directly for document tasks. You upload a PDF, ask a question, and get a seemingly accurate answer. It feels like magic.

But there’s an important distinction. These models are great for exploration and prototyping, not for running production pipelines.

Here’s what they are good for:

Trying things out quickly
Extracting ad hoc insights
Handling messy formats or inconsistent layouts
Understanding text in context

But they don’t give you:

Consistent, structured output (like clean JSON)
Confidence scores or uncertainty signals
Integration with automation tools or databases
Error handling or fallbacks
The ability to train or fine-tune the model on your specific document types or field definitions

In short: Chat-based tools are flexible and powerful, but they aren’t built for repeatable, scalable workflows. If you’re building something serious - especially with financial, legal, or compliance needs -you’ll want something more robust and tailored.

What to look for in a tool

If you're choosing a platform for document extraction, look for one that:

Supports LLM-based extraction, not just OCR
Gives you confidence estimates per field (even approximate is better than nothing)
Lets you flag and correct uncertain fields, ideally with feedback loops
Plays well with the rest of your stack (like Zapier, Make, or webhooks)

Some tools (like Cradl.ai) offer this out of the box: LLM-powered extraction, built-in review workflows, and integrations that let you automate from end to end.

Final thoughts

LLM-based OCR is a breakthrough for no-code and citizen developers. You can finally build flows that understand documents — not just read them.

But understanding doesn’t mean infallibility.

Use confidence scoring, review uncertain cases, and let automation handle the rest. That’s how you move fast without breaking things.