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166 lines
9.9 KiB
Markdown
166 lines
9.9 KiB
Markdown
# Intercom: Guardrails and Fix Patterns
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<details>
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<summary><strong>🧭 Quick Return to Map</strong></summary>
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<br>
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> You are in a sub-page of **Chatbots & CX**.
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> To reorient, go back here:
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>
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> - [**Chatbots & CX** — customer dialogue flows and conversational stability](./README.md)
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> - [**WFGY Global Fix Map** — main Emergency Room, 300+ structured fixes](../README.md)
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> - [**WFGY Problem Map 1.0** — 16 reproducible failure modes](../../README.md)
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>
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> Think of this page as a desk within a ward.
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> If you need the full triage and all prescriptions, return to the Emergency Room lobby.
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</details>
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Use this page when your Intercom bot blends **Fin** (AI Agent), **Custom Bots**, **Help Center** articles, and **webhooks** hitting your RAG stack. The checks localize failures to the exact layer and jump you to the right WFGY fix page. All links are text-hyperlinks, absolute to GitHub.
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## Open these first
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* Visual map and recovery: [rag-architecture-and-recovery.md](https://github.com/onestardao/WFGY/blob/main/ProblemMap/rag-architecture-and-recovery.md)
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* End-to-end retrieval knobs: [retrieval-playbook.md](https://github.com/onestardao/WFGY/blob/main/ProblemMap/retrieval-playbook.md)
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* Traceability: [retrieval-traceability.md](https://github.com/onestardao/WFGY/blob/main/ProblemMap/retrieval-traceability.md)
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* Data schema locks: [data-contracts.md](https://github.com/onestardao/WFGY/blob/main/ProblemMap/data-contracts.md)
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* Embedding vs meaning: [embedding-vs-semantic.md](https://github.com/onestardao/WFGY/blob/main/ProblemMap/embedding-vs-semantic.md)
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* Hallucination and chunk boundaries: [hallucination.md](https://github.com/onestardao/WFGY/blob/main/ProblemMap/hallucination.md)
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* Long chains and entropy: [context-drift.md](https://github.com/onestardao/WFGY/blob/main/ProblemMap/context-drift.md), [entropy-collapse.md](https://github.com/onestardao/WFGY/blob/main/ProblemMap/entropy-collapse.md)
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* Prompt injection and tool schema: [prompt-injection.md](https://github.com/onestardao/WFGY/blob/main/ProblemMap/prompt-injection.md)
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* Multi-agent handoffs: [Multi-Agent\_Problems.md](https://github.com/onestardao/WFGY/blob/main/ProblemMap/Multi-Agent_Problems.md)
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* Boot order traps: [bootstrap-ordering.md](https://github.com/onestardao/WFGY/blob/main/ProblemMap/bootstrap-ordering.md), [deployment-deadlock.md](https://github.com/onestardao/WFGY/blob/main/ProblemMap/deployment-deadlock.md), [predeploy-collapse.md](https://github.com/onestardao/WFGY/blob/main/ProblemMap/predeploy-collapse.md)
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## Core acceptance (CX)
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* ΔS(question, retrieved) ≤ 0.45
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* Coverage ≥ 0.70 to the target section
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* λ remains convergent across 3 paraphrases and 2 seeds
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* E\_resonance stays flat over long sessions
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---
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## Fix in 60 seconds
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1. **Measure ΔS**
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Compute ΔS(question, retrieved) and ΔS(retrieved, expected anchor).
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Stable < 0.40, transitional 0.40–0.60, risk ≥ 0.60.
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2. **Probe λ\_observe**
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Vary k and reorder prompt headers. If λ flips on harmless paraphrases, lock schema and clamp with BBAM.
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3. **Apply module**
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* Retrieval drift → **BBMC** + [retrieval-traceability.md](https://github.com/onestardao/WFGY/blob/main/ProblemMap/retrieval-traceability.md) + [data-contracts.md](https://github.com/onestardao/WFGY/blob/main/ProblemMap/data-contracts.md)
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* Reasoning collapse in long chats → **BBCR** bridge + **BBAM**; verify with [context-drift.md](https://github.com/onestardao/WFGY/blob/main/ProblemMap/context-drift.md)
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* Dead ends in toolchains → **BBPF** alternate paths
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4. **Verify**
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Three paraphrases reach coverage ≥ 0.70 and ΔS ≤ 0.45. λ convergent on two seeds.
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---
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## Typical Intercom symptoms → exact fix
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* **Fin answers without citing the right Help Center article**
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Analyzer/metric mismatch or fragmented store feeding Fin.
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→ [embedding-vs-semantic.md](https://github.com/onestardao/WFGY/blob/main/ProblemMap/embedding-vs-semantic.md), [patterns/pattern\_vectorstore\_fragmentation.md](https://github.com/onestardao/WFGY/blob/main/ProblemMap/patterns/pattern_vectorstore_fragmentation.md)
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* **Resolution Bot hands off to human too early or loops**
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Boot order or version skew between content sync and bot.
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→ [bootstrap-ordering.md](https://github.com/onestardao/WFGY/blob/main/ProblemMap/bootstrap-ordering.md), [deployment-deadlock.md](https://github.com/onestardao/WFGY/blob/main/ProblemMap/deployment-deadlock.md)
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* **Webhook returns 200 but the bot state drifts**
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Tool JSON schema too loose; free-text in arguments.
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→ [data-contracts.md](https://github.com/onestardao/WFGY/blob/main/ProblemMap/data-contracts.md), [prompt-injection.md](https://github.com/onestardao/WFGY/blob/main/ProblemMap/prompt-injection.md)
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* **High similarity, wrong snippet**
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Metric mismatch or hybrid query split between Help Center and external KB.
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→ [retrieval-playbook.md](https://github.com/onestardao/WFGY/blob/main/ProblemMap/retrieval-playbook.md), [patterns/pattern\_query\_parsing\_split.md](https://github.com/onestardao/WFGY/blob/main/ProblemMap/patterns/pattern_query_parsing_split.md)
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* **Long threads become inconsistent after 20–40 turns**
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Entropy rises with chain length; memory writes collide.
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→ [context-drift.md](https://github.com/onestardao/WFGY/blob/main/ProblemMap/context-drift.md), [entropy-collapse.md](https://github.com/onestardao/WFGY/blob/main/ProblemMap/entropy-collapse.md), [Multi-Agent\_Problems.md](https://github.com/onestardao/WFGY/blob/main/ProblemMap/Multi-Agent_Problems.md)
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* **Jailbreak or confident bluffing**
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Missing fences and cite-then-explain rules.
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→ [bluffing.md](https://github.com/onestardao/WFGY/blob/main/ProblemMap/bluffing.md), [retrieval-traceability.md](https://github.com/onestardao/WFGY/blob/main/ProblemMap/retrieval-traceability.md)
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---
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## Minimal webhook recipe
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1. **Warm-up fence**
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Check `VECTOR_READY`, `INDEX_HASH`, secrets; short-circuit if not ready.
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See [bootstrap-ordering.md](https://github.com/onestardao/WFGY/blob/main/ProblemMap/bootstrap-ordering.md).
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2. **Retrieval step**
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Call your retriever with explicit metric and consistent analyzer. Return `snippet_id`, `section_id`, `source_url`, `offsets`, `tokens`.
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3. **ΔS probe**
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Compute ΔS(question, retrieved). If ≥ 0.60, mark `needs_fix=true`.
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4. **LLM answer step**
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LLM reads TXT OS and WFGY schema. Enforce cite-then-explain across the retrieved set.
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5. **Trace sink**
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Store `question`, `ΔS`, `λ_state`, `INDEX_HASH`, `snippet_id`, `dedupe_key`.
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---
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## Copy-paste prompt for your Intercom webhook
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```
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You have TXT OS and the WFGY Problem Map loaded.
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My Intercom context:
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- channel: messenger | email | mobile
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- bot: Fin | Custom Bot | Resolution Bot
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- retrieved: {k} snippets {snippet_id, section_id, source_url, offsets, tokens}
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User question: "{user_question}"
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Do:
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1) Enforce cite-then-explain. If citations are missing or cross-section, fail fast and return the minimal fix tip.
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2) If ΔS(question, retrieved) ≥ 0.60, propose the smallest structural repair
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referencing: retrieval-playbook, retrieval-traceability, data-contracts, rerankers.
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3) Return JSON:
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{ "answer": "...", "citations": [...], "λ_state": "→|←|<>|×", "ΔS": 0.xx, "next_fix": "..." }
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Keep it short and auditable.
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```
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---
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### 🔗 Quick-Start Downloads (60 sec)
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| Tool | Link | 3-Step Setup |
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| -------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------ | ---------------------------------------------------------------------------------------- |
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| **WFGY 1.0 PDF** | [Engine Paper](https://github.com/onestardao/WFGY/blob/main/I_am_not_lizardman/WFGY_All_Principles_Return_to_One_v1.0_PSBigBig_Public.pdf) | 1️⃣ Download · 2️⃣ Upload to your LLM · 3️⃣ Ask “Answer using WFGY + \<your question>” |
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| **TXT OS (plain-text OS)** | [TXTOS.txt](https://github.com/onestardao/WFGY/blob/main/OS/TXTOS.txt) | 1️⃣ Download · 2️⃣ Paste into any LLM chat · 3️⃣ Type “hello world” — OS boots instantly |
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---
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<!-- WFGY_FOOTER_START -->
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### Explore More
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| Layer | Page | What it’s for |
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| --- | --- | --- |
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| ⭐ Proof | [WFGY Recognition Map](/recognition/README.md) | External citations, integrations, and ecosystem proof |
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| ⚙️ Engine | [WFGY 1.0](/legacy/README.md) | Original PDF tension engine and early logic sketch (legacy reference) |
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| ⚙️ Engine | [WFGY 2.0](/core/README.md) | Production tension kernel for RAG and agent systems |
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| ⚙️ Engine | [WFGY 3.0](/TensionUniverse/EventHorizon/README.md) | TXT based Singularity tension engine (131 S class set) |
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| 🗺️ Map | [Problem Map 1.0](/ProblemMap/README.md) | Flagship 16 problem RAG failure taxonomy and fix map |
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| 🗺️ Map | [Problem Map 2.0](/ProblemMap/wfgy-rag-16-problem-map-global-debug-card.md) | Global Debug Card for RAG and agent pipeline diagnosis |
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| 🗺️ Map | [Problem Map 3.0](/ProblemMap/wfgy-ai-problem-map-troubleshooting-atlas.md) | Global AI troubleshooting atlas and failure pattern map |
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| 🧰 App | [TXT OS](/OS/README.md) | .txt semantic OS with fast bootstrap |
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| 🧰 App | [Blah Blah Blah](/OS/BlahBlahBlah/README.md) | Abstract and paradox Q&A built on TXT OS |
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| 🧰 App | [Blur Blur Blur](/OS/BlurBlurBlur/README.md) | Text to image generation with semantic control |
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| 🏡 Onboarding | [Starter Village](/StarterVillage/README.md) | Guided entry point for new users |
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If this repository helped, starring it improves discovery so more builders can find the docs and tools.
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[](https://github.com/onestardao/WFGY)
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<!-- WFGY_FOOTER_END -->
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