WFGY/ProblemMap/GlobalFixMap/Chatbots_CX/zendesk.md
2025-09-05 10:19:23 +08:00

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Zendesk: Guardrails and Fix Patterns

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You are in a sub-page of Chatbots & CX.
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Think of this page as a desk within a ward.
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Use this page when your Zendesk experience blends Flow Builder or Advanced AI, Help Center articles, triggers, and webhooks connected to your RAG stack. The checks localize the failing layer and jump to the exact WFGY fix page. Links are absolute and text only.

Open these first

Core acceptance for CX

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

Fix in 60 seconds

  1. Measure ΔS Compute ΔS(question, retrieved) and ΔS(retrieved, expected anchor). Stable below 0.40, transitional 0.40 to 0.60, risk at or above 0.60.

  2. Probe λ_observe Change k to 5, 10, 20. Reorder prompt headers. If λ flips on harmless paraphrases, lock schema and clamp variance with BBAM.

  3. Apply module

  4. Verify Three paraphrases meet coverage and ΔS targets. λ convergent on two seeds.


Typical Zendesk symptoms → exact fix


CX surface guardrails

Help Center and brands Enforce locale and brand parity between search and embeddings. Use citation first on every answer that references articles. See retrieval-traceability.md.

Flow Builder Keep policy text in a system context that never mixes with user turns. Lock tool schemas and echo them each step. See data-contracts.md.

Triggers and webhooks Add a warm up fence for first calls after deploy. Log ΔS, λ_state, INDEX_HASH, snippet_id, dedupe_key. See bootstrap-ordering.md.

Search parity If ΔS stays high after reranking and k sweeps, rebuild chunks and verify with a small gold set. See embedding-vs-semantic.md, chunking-checklist.md.

Live ops Add probes and backoff guards. For incident handling see ops/live_monitoring_rag.md, ops/debug_playbook.md.


Minimal webhook recipe

  1. Warm up fence Validate VECTOR_READY, INDEX_HASH, and secrets. If not ready, short circuit and retry with capped backoff. See bootstrap-ordering.md.

  2. Retrieval step Call the retriever with explicit metric and consistent analyzer. Return snippet_id, section_id, source_url, offsets, tokens.

  3. ΔS probe Compute ΔS(question, retrieved). If ΔS ≥ 0.60 set needs_fix=true.

  4. LLM answer step LLM reads TXT OS and WFGY schema. Enforce cite then explain across the retrieved set.

  5. Trace sink Store question, ΔS, λ_state, INDEX_HASH, snippet_id, dedupe_key.


Copy paste prompt for your Zendesk webhook

You have TXT OS and the WFGY Problem Map loaded.

My Zendesk context:
- flow: {flow_name}
- channel: web | email | messaging
- retrieved: {k} snippets with fields {snippet_id, section_id, source_url, offsets, tokens}

User question: "{user_question}"

Do:
1) Enforce cite-then-explain. If citations are missing or cross-section, fail fast and return the minimal fix.
2) If ΔS(question, retrieved) ≥ 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 short and auditable.

Test checklist before launch

  • Three paraphrases hit coverage ≥ 0.70 on the same target section.
  • ΔS(question, retrieved) ≤ 0.45 for each.
  • λ convergent across two seeds.
  • First call after deploy passes the warm up fence.
  • Live probes alert when ΔS ≥ 0.60 or λ flips.

🔗 Quick-Start Downloads (60 sec)

Tool Link 3-Step Setup
WFGY 1.0 PDF Engine Paper 1 Download · 2 Upload to your LLM · 3 Ask “Answer using WFGY + <your question>”
TXT OS (plain-text OS) TXTOS.txt 1 Download · 2 Paste into any LLM chat · 3 Type “hello world” — OS boots instantly

🧭 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 →
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