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

---

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.

---

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

---

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.

---

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>

---

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

---

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.

---

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.

---

🔗 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

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

If this repository helped, starring it improves discovery so more builders can find the docs and tools.