🧭 Quick Return to Map
You are in a sub-page of Chatbots & CX.
To reorient, go back here:
- 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 CX or chatbot runs inside Twilio Studio flows and you see brittle retrieval, duplicate executions, or unstable tool calls. Each symptom maps to a structural fix in the WFGY Problem Map so you can verify with measurable targets.
- Visual map and recovery: rag-architecture-and-recovery.md
- End to end retrieval knobs: retrieval-playbook.md
- Why this snippet: retrieval-traceability.md
- Ordering control: rerankers.md
- Embedding vs meaning: embedding-vs-semantic.md
- Long chains and entropy: context-drift.md, entropy-collapse.md
- JSON and schema locks: data-contracts.md, prompt-injection.md
- Bootstrap and deploy issues: bootstrap-ordering.md, deployment-deadlock.md, predeploy-collapse.md
- Patterns: pattern_bootstrap_deadlock.md, pattern_query_parsing_split.md, pattern_vectorstore_fragmentation.md
- ΔS(question, retrieved) ≤ 0.45
- Coverage ≥ 0.70 to the target section
- λ remains convergent across three paraphrases and two seeds
- E_resonance flat on long windows
-
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
Vary k in retrieval and reorder prompt headers. If λ flips on harmless changes, lock the schema and clamp variance with BBAM. -
Apply the module
- Retrieval drift → BBMC plus data-contracts.md
- Reasoning collapse → BBCR bridge plus BBAM. Validate with logic-collapse.md
- Dead ends in long runs → BBPF alternate paths
-
Webhook loops or duplicate executions
StatusCallback, Studio Run Function, and call message webhooks can reenter the flow. Add idempotency and a warm up fence.
Open: pattern_bootstrap_deadlock.md, bootstrap-ordering.md -
First call after deploy crashes or uses wrong env
Functions cold boot or secret not loaded. Run a readiness gate before RAG.
Open: predeploy-collapse.md -
High similarity yet wrong meaning
Metric mismatch or fragmented store.
Open: embedding-vs-semantic.md, pattern_vectorstore_fragmentation.md -
Citations do not line up with the shown section
Enforce cite then explain and store offsets.
Open: retrieval-traceability.md, data-contracts.md -
Hybrid retrievers worse than single
HyDE plus BM25 split or mis weighted rerank.
Open: pattern_query_parsing_split.md, rerankers.md -
Long Studio flows flip answers between runs
Headers reorder or entropy rises after many steps.
Open: context-drift.md, entropy-collapse.md -
Tool calls produce partial JSON or free text
Lock tool schemas and echo the contract on every call.
Open: data-contracts.md, prompt-injection.md
-
Warm up fence
CheckVECTOR_READY,INDEX_HASH, and secrets before any RAG or Function. If not ready, short circuit and retry with capped backoff.
Spec: bootstrap-ordering.md -
Idempotency and dedupe
Computededupe_key = sha256(sessionId + revision + index_hash). Store in a KV or Sync. Drop duplicates before side effects. -
RAG boundary contract
Requiresnippet_id,section_id,source_url,offsets,tokens. Enforce cite then explain.
Spec: data-contracts.md and retrieval-traceability.md -
Observability probes
Log ΔS(question, retrieved) and λ by step. Alert on ΔS ≥ 0.60 or λ divergent.
See: rag-architecture-and-recovery.md -
Regression gate
Require coverage ≥ 0.70 and ΔS ≤ 0.45 on three paraphrases before publishing the flow.
-
Three paraphrase probe
Ask the same question three ways. If λ flips on harmless paraphrase, clamp with BBAM and tighten the snippet schema. -
Anchor triangulation
Compare ΔS to the expected anchor and to a decoy section. If both are close, re chunk and re embed. -
Chain length audit
If entropy rises after many widgets or Functions, split the plan and re join with a BBCR bridge.
You have TXT OS and the WFGY Problem Map.
Context:
* Channel: {sms|whatsapp|voice}
* Studio flow: {flow\_name} run {run\_id}
* Retrieval fields: {snippet\_id, section\_id, source\_url, offsets, tokens}
My issue in this run:
* symptom: \[one line]
* traces: ΔS(question,retrieved)=..., ΔS(retrieved,anchor)=..., λ across 3 paraphrases
Do:
1. Name the failing layer and why.
2. Point me to the exact WFGY fix page.
3. Give minimal steps to push ΔS ≤ 0.45 and keep λ convergent.
4. Provide a reproducible test I can run inside this flow.
Use BBMC, BBCR, BBPF, BBAM when relevant.
| 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 |
| 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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