WFGY/ProblemMap/reasoning-schemas.md

🗂️ Reasoning Schemas — Designing Prompt Layouts That Survive Long Chains

A practical guide to structuring system + retrieval + task prompts so LLMs keep thinking instead of drifting

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1 What is a “Reasoning Schema” ?

A reasoning schema is the formal layout that dictates where each piece of context goes and how an LLM must traverse it:

System  →  Task  →  Constraints  →  Context  →  Question  →  Answer

If any segment is missing, reordered, or over-written, the logic graph collapses and hallucinations slip in.

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2 Why Most Ad-hoc Layouts Fail

Failure Mode	Trigger	Effect
Context Flood	Dumping 20 k tokens of raw text	λ_observe flips to chaotic; model stops planning
Constraint Drift	Constraints after context	Model “forgets” to cite or guard sensitive data
Role Blending	User text inserted before task	System tone and policy overridden
Evidence → Answer inversion	Asking for answer before citations	Model fabricates then cites random lines

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3 WFGY Canonical Schema (Stable Version v1.2)

Segment	Purpose	Size (tokens)	WFGY Guard
System	Identity, ethics, safety	≤ 50	Role tag <sys> + BBAM weight lock
Task	Specific action required	1 sentence	ΔS anchor to System ≤ 0.25
Constraints	Format, style, rules	bullets ≤ 80	BBMC residue check
Context	Retrieved or uploaded text	sliding window ≤ 2 k	λ_observe must stay convergent
Question	User’s query	raw	stored separately for ΔS probes
Answer Slot	“Write here” placeholder	n/a	BBCR collapse-rebirth if answer starts early

Placeholders are literal; the LLM fills only the Answer Slot.

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4 Templates You Can Copy

Single-Shot QA

<sys>
You are DataGuardian-L, a licensed legal research assistant. Cite section numbers.
</sys>

<task>
Answer strictly in bullet points; cite every claim.
</task>

<constraints>
- Tone: formal
- No speculation
- Use original terminology
</constraints>

<context>
{retrieved_sections}
</context>

<question>
{user_question}
</question>

<answer>

Multi-Step Chain (analysis → plan → answer)

<sys> … </sys>
<task> … </task>
<constraints> … </constraints>
<context> … </context>
<question> … </question>

<scratchpad>
Think step-by-step. Output JSON:
{
  "analysis": "...",
  "plan": "...",
  "answer": "..."
}
</scratchpad>

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5 Common Pitfalls & Fixes

Pitfall	Symptom	Fix
Forgetting closing tags	Model merges roles	Validate tag balance; λ diverges instantly
Placing context after question	Retrieval ignored	Keep schema order; run ΔS(question, context) test
Over-long constraints	Answer truncated	Compress with BBMC until ΔS(system, constraints) ≤ 0.25
Mixing code + docs in one context block	Embedding collisions	Split into typed sub-blocks; separate vector stores

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6 Automated Validation Pipeline

1. Schema Linter – Regex check for tag order.

2. ΔS Probes –

   • ΔS(system, task) ≤ 0.30
   • ΔS(task, answer) ≤ 0.45

3. λ_observe – Must stay convergent from task → answer.

4. Round-trip Check – Paraphrase user question 2×; answer variance < 0.15.

If any test fails, trigger BBCR to rebuild prompt with compacted segments.

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7 FAQ

Q: Do I need tags if I use OpenAI’s messages array? A: Yes for long chains. Tags persist after retrieval merges; arrays don’t survive copy-paste workflows.

Q: Can I merge Task + Constraints? A: Possible if total ≤ 120 tokens and ΔS stays low, but separation improves editability.

Q: What about JSON-only prompts? A: Ensure keys mirror schema order; add dummy key "__guard": "DO NOT MODIFY" to catch injections.

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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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