WFGY/ProblemMap/GlobalFixMap/LLM_Providers

LLM Providers: Guardrails and Fix Patterns

Use this hub when failures smell “provider specific” but root cause is actually schema, retrieval, orchestration, or eval drift. Every fix maps back to WFGY pages with measurable targets, so you can verify without changing infra.

Open these first

• Visual map and recovery: RAG Architecture & Recovery
• End-to-end retrieval knobs: Retrieval Playbook
• Why this snippet (traceability schema): Retrieval Traceability
• Ordering control: Rerankers
• Embedding vs meaning: Embedding ≠ Semantic
• Hallucination and chunk boundaries: Hallucination
• Long chains and entropy: Context Drift, Entropy Collapse
• Structural collapse and recovery: Logic Collapse
• Snippet and citation schema: Data Contracts
• Live ops: Live Monitoring for RAG, Debug Playbook
• Boot order issues: Bootstrap Ordering, Deployment Deadlock, Pre-Deploy Collapse

Core acceptance

• ΔS(question, retrieved) ≤ 0.45
• Coverage ≥ 0.70 for the target section
• λ remains convergent across three paraphrases and two seeds
• E_resonance stays flat on long windows

Typical provider symptoms → exact fix

Symptom	Likely cause	Open this
JSON mode breaks, invalid objects	schema too loose or nested tool calls	Data Contracts, Logic Collapse
Tool calls loop or stall	agent role drift, missing timeouts	Multi-Agent Problems, role-drift deep dive
High similarity yet wrong snippet	metric mismatch or fragmented store	Embedding ≠ Semantic, Vectorstore Fragmentation
Answers flip between runs	prompt headers reorder and λ flips	Context Drift, Retrieval Traceability
Hybrid retrievers worse than single	query parsing split, mis-weighted rerank	Query Parsing Split, Rerankers
Jailbreaks or bluffing	overconfidence and missing fences	Bluffing Controls, Retrieval Traceability

Fix in 60 seconds

1. Measure ΔS
   Compute ΔS(question, retrieved) and ΔS(retrieved, expected anchor).
   Stable < 0.40, transitional 0.40–0.60, risk ≥ 0.60.

2. Probe λ_observe
   Vary k and prompt headers. If λ flips, lock the schema and apply a BBAM variance clamp.

3. Apply the module

• Retrieval drift → BBMC + Data Contracts
• Reasoning collapse → BBCR bridge + BBAM
• Dead ends in long runs → BBPF alternate paths

4. Verify
   Coverage ≥ 0.70 on three paraphrases. λ convergent on two seeds.

Quick routes to per-provider pages

• OpenAI: openai.md
• Azure OpenAI: azure_openai.md
• Anthropic: anthropic.md
• Anthropic Claude: anthropic_claude.md
• Google Gemini: gemini.md
• Google Vertex AI: google_vertex_ai.md
• Mistral: mistral.md
• Meta Llama: meta_llama.md
• Cohere: cohere.md
• DeepSeek: deepseek.md
• Kimi: kimi.md
• Groq (LPU inference): groq.md
• xAI Grok: grok_xai.md
• AWS Bedrock: aws_bedrock.md
• OpenRouter: openrouter.md
• Together: together.md

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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 + ”
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

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 →

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