Chunking Checklist

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Long-context retrieval often fails not at the level of whole documents but at the joins between chunks.
This checklist enforces stable, reproducible chunking so citations line up and entropy does not melt across boundaries.

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When to use

• Citations drift by a few lines between runs.
• Long transcripts lose alignment after OCR or parsing.
• Model answers cover the right fact but cite the wrong block.
• ΔS spikes exactly at chunk joins.
• Different agents disagree on chunk IDs.

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Core acceptance targets

• Each join ΔS ≤ 0.50.
• Overall ΔS(question, retrieved) ≤ 0.45.
• Coverage ≥ 0.70 of intended section.
• λ remains convergent across 3 paraphrases.
• Each chunk has immutable chunk_id, start_line, end_line.

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Checklist for stable chunking

• Deterministic boundaries
  Split on semantic units (sections, paragraphs, headings). Never by raw token count alone.

• Overlap fence
  Add 10–15% overlap at joins. Enforce consistent overlap across every run.

• Immutable IDs
  Generate chunk_id = sha256(doc_id + start_line + end_line). Store and reuse.

• Audit trail
  Store {chunk_id, start_line, end_line, source_url, tokens} for every chunk.

• Normalization
  Apply Unicode NFC, collapse whitespace, unify casing.

• Confidence gating
  Drop OCR or parsing lines with low confidence before chunking.

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Fix in 60 seconds

1. Re-chunk corpus using semantic units.
2. Apply overlap fence and store immutable chunk IDs.
3. Run ΔS probes at joins. If ΔS > 0.50, re-check boundaries.
4. Store all chunk metadata in trace logs.
5. Require cite-then-answer. Reject any orphan chunk references.

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Copy-paste prompt

You have TXT OS and the WFGY Problem Map.

Task: enforce stable chunking.

Protocol:

1. Verify each snippet has {chunk\_id, start\_line, end\_line, section\_id, source\_url}.
2. Reject orphans: if citation lacks chunk\_id, stop and request fix.
3. Require cite-then-answer.
4. Probe ΔS across joins, keep ≤ 0.50.
5. Report ΔS(question,retrieved), ΔS(joins), and λ state.

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Common failure signals

• Answers cite correct fact but wrong block → chunk IDs not stable.
• ΔS spikes exactly at joins → overlap missing.
• OCR transcripts break alignment → normalization skipped.
• Multi-agent systems cite different chunk IDs → contract drift.

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🔗 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

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Explore More

Layer	Page	What it’s for
⭐ Proof	WFGY Recognition Map	External citations, integrations, and ecosystem proof
⚙️ Engine	WFGY 1.0	Original PDF tension engine and early logic sketch (legacy reference)
⚙️ Engine	WFGY 2.0	Production tension kernel for RAG and agent systems
⚙️ Engine	WFGY 3.0	TXT based Singularity tension engine (131 S class set)
🗺️ Map	Problem Map 1.0	Flagship 16 problem RAG failure taxonomy and fix map
🗺️ Map	Problem Map 2.0	Global Debug Card for RAG and agent pipeline diagnosis
🗺️ Map	Problem Map 3.0	Global AI troubleshooting atlas and failure pattern map
🧰 App	TXT OS	.txt semantic OS with fast bootstrap
🧰 App	Blah Blah Blah	Abstract and paradox Q&A built on TXT OS
🧰 App	Blur Blur Blur	Text to image generation with semantic control
🏡 Onboarding	Starter Village	Guided entry point for new users

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