Hi everyone,
I’m working on an n8n-based workflow where I need to extract structured data from PDFs whose layout varies a lot from document to document.
The specific challenge is fields that are conceptually the same but:
appear in different positions on the page
use different labels / wording
are sometimes in tables, sometimes in text blocks
are not always explicitly declared (partial info, cumulative info, etc.)
In my case, this is not about OCR quality (OCR works fine), but about semantic extraction reliability.
What I’m currently doing
OCR / text extraction → plain text
LLM-based Information Extraction nodes
Some custom JS code to normalize and validate outputs
Fallback to human confirmation when confidence is low
This works most of the time, but for edge cases the LLM still:
picks the wrong occurrence
overfits to patterns that are not always present
or confidently returns an incorrect value
My questions to the community
Are there more robust patterns than a single “Information Extraction” step?
For example:
multi-pass extraction (candidate detection → ranking → validation)
combining regex / heuristics with LLM reasoning
confidence scoring instead of hard extraction
In n8n specifically, do you usually:
rely on LLM nodes only?
or split the problem into:
deterministic code first
LLM only for ambiguous cases?
For highly variable documents, is it considered best practice to:
extract all possible candidates first
then decide downstream which one is correct?
rather than trying to extract “the right one” in a single step?
Are there alternative nodes / tools / approaches you’ve found more reliable than classic IE:
RAG-style approaches on the document text
page-by-page reasoning
rule-based pre-filtering before LLMs
hybrid pipelines (code + LLM + human-in-the-loop)
Constraint
This is a production workflow, so:
determinism matters
silent hallucinations are unacceptable
human fallback is allowed, but only as a last step
I’m mainly looking for architectural patterns and best practices, not prompt tweaks.
Any real-world experience or references would be extremely helpful.
Thanks!
for variable layouts, two things helped most:
give the LLM a list of known label aliases in the prompt — “this field may also appear as X, Y, Z”. cuts wrong-occurrence errors a lot.
also ask the model to return a confidence score alongside each value, then auto-route low-confidence ones to human review. cleaner than trying to catch errors after the fact.
Late to this but it’s a great architectural question, and I’ve spent the past year on exactly it (building a product in this space — Entity Enricher — so, disclosed bias). The patterns that actually moved reliability for me, roughly in your taxonomy:
Confidence scoring: self-reported confidence from a single model is nearly worthless — models are most confident exactly when hallucinating. The signal that works is cross-model disagreement: run extraction through 2 independent models and diff field-by-field. Fields they agree on are almost always right; fields they disagree on are your low-confidence set. That’s a measured signal, not a vibe, and it’s the cleanest trigger for your human-in-the-loop step.
Multi-pass: yes, but split by domain rather than candidate-then-rank. One focused prompt per group of related fields (amounts, parties, dates) beats one mega-prompt for the whole schema — narrower scope gives the model less room to pick the wrong occurrence.
Preventing confident wrong values: two cheap wins. (1) Make fields nullable and instruct “prefer null over guessing” — most silent hallucinations are schema pressure, the model filling a required field it has no answer for. Missing data is recoverable; wrong data isn’t. (2) Validate against the schema and feed errors back to the model for self-correction instead of failing the run.
The disagreement-resolution step can also be automated: rule-based voting for easy cases, an arbiter LLM for the rest, with the reasoning logged per field so “why is this value here” is always answerable. That audit trail turned out to matter as much as accuracy for production sign-off.