Document AI has three separate jobs: read what is physically on the page (Layer 1), work out which domain entities and relations that content represents (Layer 2), and answer the question a specific workflow cares about (Layer 3). Engineer Janos Tolgyesi, in an article on DEV Community, argues that these layers should stay explicit and that you should never skip a layer. This piece explains the model, where it helps, and what the argument does and does not prove.
The three layers at a glance
Each layer answers a different kind of question, and each has a different shelf life. The reuse profile is the part that drives most design decisions.
| Layer | Role | Question it answers | Typical contents | Reuse across workflows |
|---|---|---|---|---|
| 1 | Intrinsic structure (perception) | What is physically on the page? | Pages, blocks, tables, reading order, sections, signatures, page geometry | Fully reusable |
| 2 | Domain entities and relations (grounding) | What do these elements mean in the domain? | Parties, dates, amounts, issuing authorities, cross-references, defined terms | Partially reusable |
| 3 | Workflow-specific knowledge (inference) | What does this task need to conclude? | Duplicate-payment verdicts, enforceability judgments, board summaries of a filing | Not reusable |
Layer 1: structure that any workflow can use
Documents share structural features even when their subjects differ. An invoice, a contract and a regulatory filing all have pages, tables, headings and a reading order. Capturing that structure once makes it a reusable foundation. It deliberately says nothing about meaning: it records where a cell sits in a table, not whether the number in it is a tax amount.
Layer 2: grounding in domain entities
Layer 2 identifies and connects the concepts a family of documents uses. A generic upper ontology can supply common concepts, with domain extensions on top. In the author’s contract example, grounding means resolving a legal reference to a canonical identity and binding a term defined in the contract to its definition clause within that same contract.
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How the layer changes shape by document type
- Invoice: a fairly stable vocabulary: issuer, recipient, line items, amounts, tax, dates, reference number.
- Contract: a thinner stable vocabulary, with more effort going into reference resolution and defined-term binding.
- Novel: characters, places, events, coreference and chronology.
The model is therefore a way to decide what to extract and ground for a task, not a claim that one universal schema fits every document.
Layer 3: conclusions that belong to one question
Layer 3 answers the actual business question: is this payment a duplicate, is this clause enforceable, how should this filing be summarized for a board. The author treats this layer as task-shaped on purpose. Its outputs should stay attached to the workflow and question that produced them rather than being promoted into shared data.
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The rule: never skip a layer
The central warning is against sending a whole raw PDF or text dump to a language model and asking it for a workflow answer. The author’s illustrative failure chain: a table cell is misread, an amount gets attached to the wrong party, and the workflow reaches a wrong conclusion. With one opaque call, you see only the wrong answer.
With explicit stages you can ask which one failed. The author proposes separate golden datasets for the layers so each can be tested on its own: did perception capture the table correctly, did grounding assign the amount to the right party, did inference reason correctly from correct inputs.
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This is not a ban on going back to the source
A later inference step may fetch the exact clause or passage that earlier stages identified. That is a grounded lookup tied to evidence. What the rule forbids is bypassing the intermediate layers altogether.
What the evidence supports
The article is an architectural argument. It gives no measured accuracy, cost or incident figures showing that layering outperforms a single-call approach, and it points to pipeline error-propagation research (Finkel, Manning and Ng, 2006) without a quantified claim. Treat the diagnosability benefit as a reasoned design case, and measure it on your own documents.
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Keep Layer 2 sparse
When several workflows touch similar material, it is tempting to promote a popular conclusion into shared grounding. The author advises against it. The example is “surviving obligations”: due-diligence and litigation-risk reviews may both start from the same termination clause yet define or interpret the result differently. The right split:
- Shared Layer 2 keeps the clause and its grounded entities.
- Each review keeps its own judgment in its own Layer 3.
In the author’s words, keep Layer 2 sparse and Layer 3 rich and disposable. A workable test: if a fact’s meaning is the same regardless of the question being asked, it can live in Layer 2; if it depends on the question, it belongs in Layer 3.
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A precondition: stable identifiers
Layering only works if upper layers can keep pointing at the right spans. If Layer 1 identifiers change every time a document is re-extracted, for example after an OCR or model update, groundings and conclusions may stop pointing to what they were meant to. The author flags this and says a later installment will cover a document object model that survives re-extraction; that design is not part of the article discussed here. Until you have a solution, plan for how references will be remapped or invalidated when you reprocess documents.
Quick Recap
Applying the model
- For each workflow, write the final question it must answer; that defines Layer 3.
- List the stable, question-independent entities and relations that question depends on; that defines the minimum Layer 2.
- Decide what structural output Layer 1 must preserve (tables, geometry, reading order) to support those entities.
- Give each layer its own test set so failures can be localized.
- Store stable references between layers, and let Layer 3 retrieve source evidence through them when needed.
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