AWS Textract: Guardrails and Fix Patterns

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DocumentAI_OCR — document parsing and optical character recognition

WFGY Global Fix Map — main Emergency Room, 300+ structured fixes

WFGY Problem Map 1.0 — 16 reproducible failure modes

Think of this page as a desk within a ward.
If you need the full triage and all prescriptions, return to the Emergency Room lobby.

Stabilize ingestion flows with AWS Textract when parsing PDFs, invoices, or forms.
Use this when outputs fragment, lose semantic anchors, or citations drift across page boundaries. Each issue maps back to WFGY Problem Map structural fixes.

Open these first

Visual map and recovery: RAG Architecture & Recovery
Retrieval knobs: Retrieval Playbook
Citation schema: Retrieval Traceability
Embedding vs meaning: Embedding ≠ Semantic
Chunk stability: Chunking Checklist
Hallucination and span errors: Hallucination

Core acceptance

ΔS(question, retrieved) ≤ 0.45
Coverage ≥ 0.70 of target section
λ convergent across 3 paraphrases
Table and key-value forms consistent ≥ 90% of samples

Typical breakpoints → structural fix

Key–value pairs misaligned (invoices, receipts)
→ Data Contracts, Retrieval Traceability
Tables fragment into multiple OCR blocks
→ Chunking Checklist
ΔS spikes across repeated runs
Entropy in layout ordering.
→ Entropy Collapse
Citations drop anchor IDs
Post-processing trims.
→ Retrieval Traceability
Injected text hidden in form fields
→ Prompt Injection

Fix in 60 seconds

Measure ΔS between Textract output and reference text.
Enforce schema: lock page_num, bbox, kv_id, table_id.
Cross-check coverage: at least 70% of source fields retained.
Apply λ probes across runs — clamp unstable output with BBAM.
Audit layout: row/col count vs original file.

Copy-paste LLM guard prompt

I uploaded TXTOS and the WFGY Problem Map.

OCR provider: AWS Textract  
Symptoms: misaligned key-value pairs, ΔS ≥ 0.60, coverage < 0.70.  

Steps:  
1. Identify failing layer (chunking, contracts, retrieval).  
2. Point to the WFGY fix (data-contracts, chunking-checklist, retrieval-traceability).  
3. Return JSON:  
   { "citations": [...], "answer": "...", "ΔS": 0.xx, "λ_state": "<>", "next_fix": "..." }  
Keep it auditable.

When to escalate

Coverage < 0.70 even after re-chunking → verify embeddings with Embedding ≠ Semantic.
Key–value fields unstable across runs → rebuild with deterministic config, backstop with Data Contracts.
Long-form text loses anchors → apply Retrieval Traceability.

🔗 Quick-Start Downloads (60 sec)

Tool	Link	3-Step Setup
WFGY 1.0 PDF	Engine Paper	1️⃣ Download · 2️⃣ Upload to your LLM · 3️⃣ Ask “Answer using WFGY + <your question>”
TXT OS (plain-text OS)	TXTOS.txt	1️⃣ Download · 2️⃣ Paste into any LLM chat · 3️⃣ Type “hello world” — OS boots instantly

🧭 Explore More

Module	Description	Link
WFGY Core	WFGY 2.0 engine is live: full symbolic reasoning architecture and math stack	View →
Problem Map 1.0	Initial 16-mode diagnostic and symbolic fix framework	View →
Problem Map 2.0	RAG-focused failure tree, modular fixes, and pipelines	View →
Semantic Clinic Index	Expanded failure catalog: prompt injection, memory bugs, logic drift	View →
Semantic Blueprint	Layer-based symbolic reasoning & semantic modulations	View →
Benchmark vs GPT-5	Stress test GPT-5 with full WFGY reasoning suite	View →
🧙‍♂️ Starter Village 🏡	New here? Lost in symbols? Click here and let the wizard guide you through	Start →