Layout Extraction Failure Mode Analysis Prompt Template

This prompt is for engineering teams debugging document extraction pipelines. Use it when your extraction output shows signs of layout corruption: paragraphs that jump between columns mid-sentence, tables with scrambled cell order, missing callout boxes, or reading-order breaks that produce nonsense text. The prompt compares a source layout representation against the extraction output, identifies specific failure modes, classifies their severity, and suggests remediation steps. It belongs in your pipeline observability stack, not in the extraction path itself. Run it on a sample of extraction results during QA, after pipeline changes, or when downstream systems report data quality regressions.

Do not use this prompt as a real-time extraction corrector or as a substitute for fixing the underlying extraction logic. It is a diagnostic tool, not a repair tool. The prompt assumes you already have both a source layout representation (bounding boxes, reading order, element types) and the extraction output you want to audit. If you lack a ground-truth layout representation, this prompt cannot produce reliable failure analysis. For high-stakes document workflows—legal contracts, regulatory filings, clinical records—always pair the prompt's output with human review of the identified failure modes before taking corrective action on the pipeline.

The ideal user is an engineer or technical operator who understands the extraction pipeline's architecture and can act on the failure classifications. You should have access to a sample of documents where extraction quality is suspect, along with their corresponding layout metadata. Run this prompt before making pipeline changes to establish a baseline, and again after changes to verify that fixes didn't introduce new failure modes. The output is structured JSON designed to feed into your observability dashboards, regression test suites, or incident reports.

This prompt template is the core diagnostic tool for engineering teams debugging document extraction pipelines. It takes the raw extraction output and the source layout metadata and produces a structured failure report. The report classifies each issue by type (column misordering, merged regions, dropped elements, reading-order breaks), assigns a severity level, and suggests remediation steps. Use this template as the starting point for your automated quality analysis harness.

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You are a document layout extraction auditor. Your task is to compare the extracted text output against the source layout metadata and identify extraction failure modes.

## INPUT
[EXTRACTION_OUTPUT]

[LAYOUT_METADATA]

## TASK
Analyze the extraction output for the following failure modes:
1. Column misordering: Text from different columns is interleaved or sequenced incorrectly.
2. Merged regions: Adjacent but semantically distinct layout regions (e.g., body text and sidebar) are combined into a single text block.
3. Dropped elements: Content present in the layout metadata is missing from the extraction output.
4. Reading-order breaks: The linearized text flow jumps discontinuously, breaking paragraph or sentence continuity.
5. Boundary violations: Extracted text spans cross layout region boundaries, contaminating one region with content from another.

## OUTPUT_SCHEMA
Return a JSON object with the following structure:
{
  "document_id": "string",
  "analysis_timestamp": "ISO8601",
  "summary": {
    "total_regions_in_layout": "integer",
    "regions_with_errors": "integer",
    "critical_failures": "integer",
    "warnings": "integer"
  },
  "failures": [
    {
      "failure_id": "string",
      "failure_type": "column_misordering | merged_region | dropped_element | reading_order_break | boundary_violation",
      "severity": "critical | warning | info",
      "source_region_ids": ["string"],
      "extracted_text_snippet": "string",
      "expected_behavior": "string",
      "actual_behavior": "string",
      "remediation_suggestion": "string"
    }
  ]
}

## CONSTRAINTS
- Only report failures where there is clear evidence from comparing extraction output to layout metadata.
- Do not flag stylistic differences or whitespace variations as failures.
- If a region is partially extracted, classify it as a boundary_violation or dropped_element based on the proportion of missing content.
- For each failure, include a specific extracted_text_snippet that demonstrates the problem.
- If no failures are detected, return an empty failures array and set all summary counts to zero.
- Assign critical severity to failures that would cause downstream data corruption (e.g., merged financial table cells, dropped legal clauses).
- Assign warning severity to failures that degrade readability but preserve core information.

## EXAMPLES
[FAILURE_EXAMPLES]

Adapt this template by replacing the square-bracket placeholders with your pipeline-specific data. [EXTRACTION_OUTPUT] should contain the full text output from your extraction step. [LAYOUT_METADATA] should include region bounding boxes, reading order, element types, and font metadata. [FAILURE_EXAMPLES] is optional but strongly recommended: provide 2-3 annotated examples of known failure patterns from your document corpus to calibrate the model's severity judgments and failure classification. After generating the report, validate the JSON schema before ingesting it into your monitoring dashboard or alerting system.

This prompt is designed to sit after your primary layout-aware extraction step and before downstream consumers like chunking, structured extraction, or RAG ingestion. It acts as a quality gate: given the original layout metadata (bounding boxes, reading order, font info) and the extracted text output, it produces a structured failure report. The report classifies each detected issue by severity (critical, warning, info) and type (column_misorder, merged_region, dropped_element, reading_order_break), enabling your pipeline to decide whether to route the document for manual review, retry with a different extraction strategy, or accept with logged warnings.

To implement, wrap the prompt in a function that accepts two required inputs: layout_metadata (a JSON object containing page-by-page bounding boxes, font spans, and the extraction engine's declared reading order) and extracted_text (the plain text or markdown output from your extraction step). The function should call your LLM with the prompt template, parse the JSON response, and validate it against a strict schema. Critical validation checks: confirm every reported issue includes a page_number and region_id that exist in the input metadata; verify severity is one of the allowed enum values; and ensure remediation_suggestion is non-empty for all critical items. If validation fails, retry once with the validation errors appended as feedback. If the second attempt also fails, escalate the document to a human review queue rather than silently accepting a malformed analysis.

For production reliability, log every analysis result—including documents that pass with zero issues—to build a baseline of extraction health over time. Use the structured failure types to drive routing logic: critical column misorders should block ingestion and trigger a retry with a different extraction engine or prompt variant; warning merged regions can proceed but should flag the affected chunks for lower confidence weighting in RAG; info-level findings can be recorded for trend analysis without blocking the pipeline. Avoid the temptation to use this prompt as a real-time correction step—its job is diagnosis, not repair. Pair it with a separate repair prompt or a human-in-the-loop workflow for actual remediation.