🧭 Quick Return to Map

You are in a sub-page of LLM_Providers.
To reorient, go back here:

• LLM_Providers — model vendors and deployment options
• 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.

AWS Bedrock: Guardrails and Fix Patterns

Use this page when failures look provider‐specific in AWS Bedrock. Typical cases are mismatched model routing (Claude, Llama, Mistral, etc.), JSON schema drift, tool-call latency, throttle ceilings, or region/IAM issues that masquerade as “reasoning bugs.” Each fix maps to WFGY pages so you can verify with measurable targets.

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
• Ops and live checks: Live Monitoring for RAG

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Fix in 60 seconds

1. Measure ΔS

• Compute ΔS(question, retrieved) and ΔS(retrieved, expected anchor).
• Targets: stable < 0.40, transitional 0.40–0.60, risk ≥ 0.60.

2. Probe with λ_observe

• Vary k ∈ {5, 10, 20}. Flat high curve → index or metric mismatch.
• Reorder prompt headers. If ΔS spikes, lock the schema.

3. Apply the module

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

4. Verify

• Coverage to target section ≥ 0.70.
• ΔS ≤ 0.45 within three paraphrases.
• λ stays convergent across seeds and sessions.

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Typical Bedrock breakpoints (and the right fix)

• Model routing not what you think
  Invoking anthropic.claude-* vs meta.llama-* vs mistral.* changes tokenizer, max tokens, and tool-call behavior. If outputs flip between routes, pin the model id per task, then re-check with Logic Collapse and Retrieval Traceability.

• JSON schema drift in tool use
  Claude via Bedrock is strict on JSON when toolChoice is forced. Lock the output schema with Data Contracts and add a BBCR bridge step that rejects non-conformant fields.

• Latency spikes → hidden timeouts
  Region hop or Guardrails policy checks can add latency. Use small test prompts and trace λ per step. If λ diverges only when tools are enabled, set a shorter planning window and split tools by namespace. See ops/live_monitoring_rag.md.

• Bedrock “Guardrails” over-filtering
  Safety filters can truncate citations or code blocks. If citations vanish, lower the filter aggressiveness, then enforce source-only answers with Retrieval Traceability and the SCU pattern (symbolic constraint unlock).

• Context windows differ across routes
  If the same prompt collapses only on one model family, shrink the active window and re-chunk. Validate with Context Drift and Entropy Collapse.

• IAM or region misconfig → “reasoning” looks random
  On silent fallbacks or throttling, the agent loops. Install a BBCR checkpoint that asserts model id, region, and rate state before long chains. If it fails, exit early and surface infra status. See bootstrap-ordering and predeploy-collapse.

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Provider knobs and minimal recipes

• Pin model and cap tokens

  • One task, one model id. Keep a per-task max tokens map.
  • If you must swap models, add BBPF to branch at the planner step, not mid-reasoning.

• Force citations first

  • Use citation-first headers and the snippet schema from Retrieval Traceability.
  • Reject answers without snippet ids. That alone removes most “looks like hallucination” cases.

• Defuse prompt injection

  • Apply the injection checklist and keep tools off until the source set is locked. See prompt-injection.md.

• Rerank aggressively

  • Many Bedrock routes benefit from tighter top-k ordering. Use Rerankers and then re-test ΔS across 3 paraphrases.

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Escalation path

• If ΔS flat-high across k and models → rebuild index with new metric. Check embedding-vs-semantic.
• If agent deadlocks with tools → split memory namespaces and add timeouts. See Multi-Agent Problems.
• If first prod call fails after deploy → confirm ordering with bootstrap-ordering and deployment-deadlock.

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Copy-paste prompt (safe)

I uploaded TXT OS and the WFGY ProblemMap files.

My Bedrock issue:

* symptom: \[brief]
* traces: \[ΔS(question,retrieved)=..., ΔS(retrieved,anchor)=..., λ states]

Tell me:

1. which layer is failing and why,
2. which exact fix page to open from this repo,
3. the minimal steps to push ΔS ≤ 0.45 and keep λ convergent,
4. how to verify the fix with a reproducible test.

Use BBMC/BBPF/BBCR/BBAM when relevant.

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Acceptance targets

• Coverage to target section ≥ 0.70
• ΔS(question, retrieved) ≤ 0.45 within three paraphrases
• λ remains convergent across seeds and sessions
• E_resonance flat on long windows

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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

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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