# IFTTT — Guardrails and Fix Patterns
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Use this when your automation is built on **IFTTT** (Webhooks, Google Sheets, Gmail, Slack, Calendar). If flows “work” but answers are still wrong, citations are off, or behavior differs between applets and direct API tests, anchor your diagnosis here. **Acceptance targets** - ΔS(question, retrieved) ≤ 0.45 - Coverage ≥ 0.70 to the intended section or record - λ stays convergent across 3 paraphrases --- ## Typical breakpoints → exact fixes - Output sounds right but cites the wrong snippet or section Fix No.1: **Hallucination & Chunk Drift** → [Hallucination](https://github.com/onestardao/WFGY/blob/main/ProblemMap/hallucination.md) · [Retrieval Playbook](https://github.com/onestardao/WFGY/blob/main/ProblemMap/retrieval-playbook.md) - High vector similarity but wrong meaning Fix No.5: **Embedding ≠ Semantic** → [Embedding ≠ Semantic](https://github.com/onestardao/WFGY/blob/main/ProblemMap/embedding-vs-semantic.md) - Indexed facts exist (Drive/Sheets/DB) yet never appear in top-k Pattern: **Vectorstore Fragmentation** → [Vectorstore Fragmentation](https://github.com/onestardao/WFGY/blob/main/ProblemMap/patterns/pattern_vectorstore_fragmentation.md) - Can’t show “why this snippet?” from applet logs Fix No.8: **Retrieval Traceability** + snippet/citation schema → [Retrieval Traceability](https://github.com/onestardao/WFGY/blob/main/ProblemMap/retrieval-traceability.md) · [Data Contracts](https://github.com/onestardao/WFGY/blob/main/ProblemMap/data-contracts.md) - Long chains across multiple applets drift in tone or logic Fix No.3/No.9: **Context Drift** and **Entropy Collapse** → [Context Drift](https://github.com/onestardao/WFGY/blob/main/ProblemMap/context-drift.md) · [Entropy Collapse](https://github.com/onestardao/WFGY/blob/main/ProblemMap/entropy-collapse.md) - Works in dry runs, fails on schedule or from mobile triggers Infra family: **Pre-Deploy / Bootstrap / Deadlock** → [Pre-Deploy Collapse](https://github.com/onestardao/WFGY/blob/main/ProblemMap/predeploy-collapse.md) · [Bootstrap Ordering](https://github.com/onestardao/WFGY/blob/main/ProblemMap/bootstrap-ordering.md) · [Deployment Deadlock](https://github.com/onestardao/WFGY/blob/main/ProblemMap/deployment-deadlock.md) - Model answers confidently with wrong claims Fix No.4: **Bluffing / Overconfidence** → [Bluffing](https://github.com/onestardao/WFGY/blob/main/ProblemMap/bluffing.md) --- ## Minimal IFTTT pattern with WFGY checks A compact pattern that keeps **cite-first schema**, **observable retrieval**, and **ΔS/λ** validation even when steps are split across applets. ```txt Applet A — Trigger (Webhooks) - Input JSON: { question, k? } Applet B — Retrieve (Webhooks → your retriever API) - Returns: snippets[] = { snippet_id, text, source, section_id } - Store: a short-lived record (e.g., in Google Sheets or your API, keyed by request_id) Applet C — Assemble + Call LLM (Webhooks → your prompt API) SYSTEM: Cite lines before any explanation. Keep per-source fences. TASK: Answer only from the provided context. Return citations as [snippet_id]. CONTEXT: QUESTION: Applet D — WFGY Post-check (Webhooks → wfgyCheck) - Body: { question, context, answer } - Returns: { deltaS, lambda, coverage, notes } Applet E — Gate and Notify IF deltaS ≥ 0.60 OR lambda != "→" → send failure payload with trace table (snippet_id↔citation), ask to retry after fix ELSE → deliver { answer, citations[], deltaS, lambda, coverage } to user channel ```` Reference specs: [RAG Architecture & Recovery](https://github.com/onestardao/WFGY/blob/main/ProblemMap/rag-architecture-and-recovery.md) · [Retrieval Playbook](https://github.com/onestardao/WFGY/blob/main/ProblemMap/retrieval-playbook.md) · [Retrieval Traceability](https://github.com/onestardao/WFGY/blob/main/ProblemMap/retrieval-traceability.md) · [Data Contracts](https://github.com/onestardao/WFGY/blob/main/ProblemMap/data-contracts.md) --- ## IFTTT-specific gotchas * **Sheets cell truncation** hides context length. Store only `snippet_id` and a short preview in Sheets, keep full text in your API or DB. See [Data Contracts](https://github.com/onestardao/WFGY/blob/main/ProblemMap/data-contracts.md) * **Hidden tokenization** when composing prompts in plain text fields. Always assemble prompts in your API layer to enforce the cite-first schema. See [Retrieval Traceability](https://github.com/onestardao/WFGY/blob/main/ProblemMap/retrieval-traceability.md) * **Environment mismatch** between mobile and server triggers. Bootstrap checks must verify secrets, version, and index availability before the first LLM call. See [Pre-Deploy Collapse](https://github.com/onestardao/WFGY/blob/main/ProblemMap/predeploy-collapse.md) * **Ordering hazards** when multiple applets race. Add an explicit **rerank** step after per-source ΔS ≤ 0.50, then lock order. See [Rerankers](https://github.com/onestardao/WFGY/blob/main/ProblemMap/rerankers.md) * **Attachment loss** in cross-channel handoffs. Store attachment metadata and link by `request_id`, never inline large blobs into prompts. --- ## When to escalate * ΔS stays ≥ 0.60 after chunking and retrieval adjustments → rebuild index with explicit metric flags and unit normalization. [Retrieval Playbook](https://github.com/onestardao/WFGY/blob/main/ProblemMap/retrieval-playbook.md) * Answers flip between device-triggered and server-triggered runs → verify version skew, secret scope, and boot ordering. [Bootstrap Ordering](https://github.com/onestardao/WFGY/blob/main/ProblemMap/bootstrap-ordering.md) · [Deployment Deadlock](https://github.com/onestardao/WFGY/blob/main/ProblemMap/deployment-deadlock.md) ``` --- ### 🔗 Quick-Start Downloads (60 sec) | Tool | Link | 3-Step Setup | |------|------|--------------| | **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 + \” | | **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 | --- ### Explore More | Layer | Page | What it’s for | | --- | --- | --- | | Proof | [WFGY Recognition Map](/recognition/README.md) | External citations, integrations, and ecosystem proof | | Engine | [WFGY 1.0](/legacy/README.md) | Original PDF based tension engine | | Engine | [WFGY 2.0](/core/README.md) | Production tension kernel and math engine for RAG and agents | | Engine | [WFGY 3.0](/TensionUniverse/EventHorizon/README.md) | TXT based Singularity tension engine, 131 S class set | | Map | [Problem Map 1.0](/ProblemMap/README.md) | Flagship 16 problem RAG failure checklist and fix map | | Map | [Problem Map 2.0](/ProblemMap/rag-architecture-and-recovery.md) | RAG focused recovery pipeline | | Map | [Problem Map 3.0](/ProblemMap/wfgy-rag-16-problem-map-global-debug-card.md) | Global Debug Card, image as a debug protocol layer | | Map | [Semantic Clinic](/ProblemMap/SemanticClinicIndex.md) | Symptom to family to exact fix | | Map | [Grandma’s Clinic](/ProblemMap/GrandmaClinic/README.md) | Plain language stories mapped to Problem Map 1.0 | | Onboarding | [Starter Village](/StarterVillage/README.md) | Guided tour for newcomers | | App | [TXT OS](/OS/README.md) | TXT semantic OS, fast boot | | App | [Blah Blah Blah](/OS/BlahBlahBlah/README.md) | Abstract and paradox Q and A built on TXT OS | | App | [Blur Blur Blur](/OS/BlurBlurBlur/README.md) | Text to image with semantic control | | App | [Blow Blow Blow](/OS/BlowBlowBlow/README.md) | Reasoning game engine and memory demo | If this repository helped, starring it improves discovery so more builders can find the docs and tools. 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