Applied AI
AI document automation needs a human-review architecture, not just a model.
The model is one component of a safe document workflow. The product has to decide what is extracted, what is validated deterministically, what uncertainty looks like and when a person must intervene.
Keep evidence and interpretation separate.
The document is evidence. The extracted field is an interpretation of that evidence. Store both so a reviewer can see why a value was proposed and correct it without losing the source.
Prefer structured output over free-form answers.
A business workflow needs defined fields, types and allowed states. Ask the model for a schema and validate that schema outside the model.
- Dates should parse as dates.
- Amounts should include currency context where relevant.
- Identifiers should be matched against known business records.
- Required fields should fail visibly when missing.
- Enums should reject unsupported values instead of accepting creative text.
Confidence is a routing signal, not truth.
A high model confidence should not override a deterministic business-rule failure. A low-confidence field may still be correct. Use confidence and validation together to decide whether the workflow can continue automatically.
Build a review interface around decisions.
- 01
Show the source
Display the original document or relevant section beside the proposed structured value.
- 02
Highlight the reason for review
Tell the reviewer whether the issue is low confidence, missing master data, conflicting values or a business-rule failure.
- 03
Make correction explicit
Keep the proposed value and the approved value distinguishable for audit and evaluation.
- 04
Learn from corrections carefully
Use corrected examples in evaluation sets before changing prompts, models or matching logic.
Downstream actions need a separate gate.
Extraction should not automatically create payments, approve candidates, alter accounting records or post orders merely because the model returned valid JSON. High-impact actions need authorization and business controls beyond AI output.
Evaluate the system, not only the prompt.
Maintain representative examples and failure cases.
Measure errors by business impact, not just average field accuracy.
Re-run evaluations when prompts, models, parsers or master-data logic change.
Track latency and cost, but never optimise them by removing controls that protect the workflow.
Editorial note
This resource is based on XZ BYTECORE's product and workflow engineering approach. It is intended to help teams reason about software decisions; it does not replace legal, accounting, tax, employment or other professional advice where those responsibilities apply.