WFGY/ProblemMap/GlobalFixMap/Retrieval/chunk_alignment.md

Chunk Alignment — Guardrails and Fix Patterns

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• Retrieval — information access and knowledge lookup
• 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.

Make the model cite the exact evidence. This page gives you a reliable way to align chunks to semantic anchors, so retrieval points at the right spans and citations survive through generation.

References you may want open already:
RAG Architecture & Recovery · Retrieval Playbook · Chunking Checklist · Retrieval Traceability · Data Contracts · Embedding ≠ Semantic

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Acceptance targets

• ΔS(question, retrieved) ≤ 0.45
• Anchor coverage ≥ 0.70 for the cited spans
• Citation precision ≥ 0.85 and recall ≥ 0.75
• λ stays convergent across 3 paraphrases and 2 seeds

If ΔS stays in the 0.40 to 0.60 band and coverage is low, the index is probably misaligned. Rebuild with this page.

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Symptoms → exact fix

Symptom	Likely cause	Open this fix
High similarity but the cited span is near the answer, not on it	window cut ignores section headers and anchors	Chunking Checklist
Correct section id, wrong offsets	tokenizer or analyzer mismatch between write and read	Retrieval Playbook
Same answer oscillates across two adjacent chunks	stride too large, missing overlap contract	Data Contracts
Coverage good offline, poor online	fragmented store or partial ingestion	pattern_vectorstore_fragmentation.md
Good chunk, bad generation	cite then explain missing in the prompt schema	Retrieval Traceability

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Anchor method that never lies

You need a ground anchor per question. Each anchor is the minimal span that must be cited.

Anchor fields

• section_id stable across rebuilds
• snippet_id unique within section
• offsets {start_token, end_token} in the write-time tokenizer
• anchor_text for human audit
• hash over section_id + snippet_id + offsets + anchor_text

Contract spec for your pipeline:
Data Contracts

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How to align chunks to anchors

1. Normalize the analyzer
   Same lowercasing, unicode, punctuation, and stopword policy across write and read. If you change analyzers, invalidate and rebuild.

2. Choose window and stride
   Start with window 350 to 700 tokens, stride 30 to 60 percent of window. Increase stride if anchors often cross boundaries.

3. Fence by structure
   Reset window starts at structural cues like h1..h3, list starts, code fences, or paragraph breaks. This keeps semantic units together.

4. Pin anchors after chunking
   After chunks are built, re-map each anchor to an owning chunk. When an anchor spans two chunks, create a stitch record or adjust stride.

5. Write the trace fields
   Every retrieved item must carry section_id, snippet_id, offsets, tokens, index_hash.
   See: Retrieval Traceability

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PDF, HTML, and code specifics

• PDF
  Use logical reading order, not x-y positions. Collapse hyphenation. Treat figure captions as separate units. Reset at headings and table boundaries.

• HTML
  Strip boilerplate. Fence at h2/h3, li, and pre. Merge short sibling blocks to avoid tiny chunks.

• Code
  Chunk by symbol boundaries and docstrings. Keep signature plus the first paragraph together. Never split examples from the API they explain.

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A minimal rebuild checklist

• Same tokenizer family for write and read.
• Window and stride validated on 40 to 120 gold items.
• Anchor coverage ≥ 0.70 and citation precision ≥ 0.85.
• ΔS falls below 0.45 for the majority after rebuild.
• index_hash updated and logged in retrieval results.

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Alignment test you can run today

1. For each gold item, compute ΔS between the retrieved text and the anchor text.
2. Compute coverage of cited spans against the anchor offsets.
3. Compare to a decoy section with the same size and style. If ΔS is close for anchor and decoy, chunk again.

See the eval recipes:
Retrieval Evaluation Recipes

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Pseudocode: chunk and map anchors

# Pseudocode only
def chunk(doc, window=512, stride=256, fences=None, analyzer="lc"):
    toks = tokenize(doc, analyzer)          # write-time tokenizer
    boundaries = structure_fences(doc, toks, fences=fences)
    chunks = []
    i = 0
    while i < len(toks):
        start = nearest_left_fence(i, boundaries)
        end = min(start + window, len(toks))
        text = detokenize(toks[start:end])
        chunks.append({"start": start, "end": end, "text": text})
        i = start + stride
    return chunks

def map_anchor_to_chunk(anchor, chunks):
    spans = []
    for c in chunks:
        if not (anchor["end"] <= c["start"] or anchor["start"] >= c["end"]):
            spans.append({"snippet_id": id_of(c), "offsets": [
                max(anchor["start"], c["start"]),
                min(anchor["end"], c["end"])
            ]})
    return spans

Store the mapping result inside your index metadata for audit and to power coverage scoring.

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Copy-paste prompt to audit alignment

You have TXT OS and the WFGY Problem Map loaded.

Input:
- question: "<q>"
- retrieved: {section_id, snippet_id, offsets, text}
- anchor: {section_id, snippet_id, offsets, text}
- decoy: {section_id, snippet_id, offsets, text}

Tasks:
1) Check cite-then-explain is followed.
2) Report ΔS(question, retrieved) and ΔS(retrieved, anchor) with short notes.
3) Compute anchor coverage from offsets.
4) If ΔS ≥ 0.60 or coverage < 0.70, propose the minimal rebuild step referencing:
   chunking-checklist, retrieval-playbook, data-contracts, retrieval-traceability.
Return a compact JSON: { "ΔS": ..., "coverage": ..., "why": "...", "next_fix": "..." }.

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When to escalate

• You rebuild chunking and analyzers but ΔS remains high and coverage low. Open: Embedding ≠ Semantic

• Online runs drift after a deploy despite passing offline. Open: Bootstrap Ordering and Pre-Deploy Collapse

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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 based tension engine
Engine	WFGY 2.0	Production tension kernel and math engine for RAG and agents
Engine	WFGY 3.0	TXT based Singularity tension engine, 131 S class set
Map	Problem Map 1.0	Flagship 16 problem RAG failure checklist and fix map
Map	Problem Map 2.0	RAG focused recovery pipeline
Map	Problem Map 3.0	Global Debug Card, image as a debug protocol layer
Map	Semantic Clinic	Symptom to family to exact fix
Map	Grandma’s Clinic	Plain language stories mapped to Problem Map 1.0
Onboarding	Starter Village	Guided tour for newcomers
App	TXT OS	TXT semantic OS, fast boot
App	Blah Blah Blah	Abstract and paradox Q and A built on TXT OS
App	Blur Blur Blur	Text to image with semantic control
App	Blow Blow Blow	Reasoning game engine and memory demo

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