Amazon Lex: Guardrails and Fix Patterns

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Chatbots & CX — customer dialogue flows and conversational stability

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.

Use this page when your customer bot is built on Amazon Lex and wired to Lambda, Bedrock, Kendra/OpenSearch, or Amazon Connect. The checks below localize the failing layer and route you to the exact WFGY fix page.

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 and rerank: Rerankers
Embedding vs meaning: Embedding ≠ Semantic
Hallucination and chunk boundaries: Hallucination
Long chains and entropy: Context Drift, Entropy Collapse
Symbolic collapse and recovery: Logic Collapse
Prompt injection and tool schema locks: Prompt Injection
Multi-agent and handoff conflicts: Multi-Agent Problems
Boot order and deploy traps: Bootstrap Ordering, Deployment Deadlock, Pre-deploy Collapse
Snippet and citation schema: Data Contracts

Core acceptance for CX bots

ΔS(question, retrieved) ≤ 0.45
Coverage ≥ 0.70 to the target section
λ remains convergent across 3 paraphrases and 2 seeds
First reply time stable across retries; no slot backtracks

60-second fix checklist

Measure ΔS Compute ΔS(question, retrieved) and ΔS(retrieved, expected anchor). Stable < 0.40, transitional 0.40–0.60, risk ≥ 0.60.
Probe λ_observe Change k to 5, 10, 20. If ΔS stays high and flat, suspect metric or index mismatch. Reorder prompt headers. If λ flips, lock schema with Data Contracts.
Apply the module
- Retrieval drift → BBMC + Retrieval Traceability + Data Contracts
- Reasoning collapse → BBCR bridge + BBAM variance clamp, then verify with Logic Collapse
- Dead ends in long flows → BBPF alternate paths and shorten plan windows
Verify Re-run three paraphrases. Require ΔS ≤ 0.45 and convergent λ on two seeds.

Typical Lex breakpoints → exact fix

Slot filling freezes or backtracks Schema too loose or reprompts mutate meaning. → Data Contracts, Prompt Injection
Lambda tool JSON mismatch Nested tool calls or optional fields become free text. → Lock arguments with contracts, echo schema each turn. See Data Contracts
High similarity hits, wrong answer Metric or analyzer mismatch, fragmented store. → Embedding ≠ Semantic, Vectorstore Fragmentation
Kendra vs OpenSearch reruns change order Two-stage query split, reranker blind spots. → Query Parsing Split, Rerankers
Connect handoff loops or stuck queue Version skew or memory writes collide across paths. → Deployment Deadlock, Pre-deploy Collapse
Session attributes drift between turns Hidden state mutates across retries. → Multi-Agent Problems
Safety refusal hides the cited snippet Use citation-first prompting and SCU unlock. → Retrieval Traceability, Pattern: SCU
Jailbreak or confident bluffing Add fences and require cite-then-explain. → Bluffing Controls

Copy-paste Lambda prompt for the LLM step

You have TXTOS and the WFGY Problem Map loaded.

My Amazon Lex context:
- user_utterance: "{utterance}"
- retrieved: {snippet_id, section_id, source_url, offsets, tokens}
- session: {attributes...}

Do:
1) Enforce cite-then-explain. If citations are missing or cross-section, fail fast and return the minimal fix.
2) Compute ΔS(question, retrieved). If ΔS ≥ 0.60, propose the smallest structural repair
   referencing: retrieval-playbook, retrieval-traceability, data-contracts, rerankers.
3) Return JSON:
{ "answer": "...", "citations": [...], "λ_state": "→|←|<>|×", "ΔS": 0.xx, "next_fix": "..." }
Keep it auditable and short.

Observability hooks

Log per turn: ΔS(question,retrieved), ΔS(retrieved,anchor), λ_state, index_hash, dedupe_key.
Alert if ΔS ≥ 0.60 or λ flips on harmless paraphrase.
For live ops and rollback tips see Live Monitoring for RAG and Debug Playbook.

🔗 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

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