9.9 KiB
IFTTT — Guardrails and Fix Patterns
🧭 Quick Return to Map
You are in a sub-page of Automation Platforms.
To reorient, go back here:
- Automation Platforms — stabilize no-code workflows and integrations
- 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 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 ·
Retrieval Playbook -
High vector similarity but wrong meaning
Fix No.5: Embedding ≠ Semantic →
Embedding ≠ Semantic -
Indexed facts exist (Drive/Sheets/DB) yet never appear in top-k
Pattern: Vectorstore Fragmentation →
Vectorstore Fragmentation -
Can’t show “why this snippet?” from applet logs
Fix No.8: Retrieval Traceability + snippet/citation schema →
Retrieval Traceability ·
Data Contracts -
Long chains across multiple applets drift in tone or logic
Fix No.3/No.9: Context Drift and Entropy Collapse →
Context Drift ·
Entropy Collapse -
Works in dry runs, fails on schedule or from mobile triggers
Infra family: Pre-Deploy / Bootstrap / Deadlock →
Pre-Deploy Collapse ·
Bootstrap Ordering ·
Deployment Deadlock -
Model answers confidently with wrong claims
Fix No.4: Bluffing / Overconfidence →
Bluffing
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.
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:
<joined snippets with snippet_id + source + text>
QUESTION:
<user 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 · Retrieval Playbook · Retrieval Traceability · Data Contracts
IFTTT-specific gotchas
-
Sheets cell truncation hides context length. Store only
snippet_idand a short preview in Sheets, keep full text in your API or DB. See Data Contracts -
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
-
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
-
Ordering hazards when multiple applets race. Add an explicit rerank step after per-source ΔS ≤ 0.50, then lock order. See Rerankers
-
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
-
Answers flip between device-triggered and server-triggered runs → verify version skew, secret scope, and boot ordering. Bootstrap Ordering · Deployment Deadlock
---
### 🔗 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 + \<your question>” |
| **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
| Module | Description | Link |
|-----------------------|----------------------------------------------------------|----------|
| WFGY Core | WFGY 2.0 engine is live: full symbolic reasoning architecture and math stack | [View →](https://github.com/onestardao/WFGY/tree/main/core/README.md) |
| Problem Map 1.0 | Initial 16-mode diagnostic and symbolic fix framework | [View →](https://github.com/onestardao/WFGY/tree/main/ProblemMap/README.md) |
| Problem Map 2.0 | RAG-focused failure tree, modular fixes, and pipelines | [View →](https://github.com/onestardao/WFGY/blob/main/ProblemMap/rag-architecture-and-recovery.md) |
| Semantic Clinic Index | Expanded failure catalog: prompt injection, memory bugs, logic drift | [View →](https://github.com/onestardao/WFGY/blob/main/ProblemMap/SemanticClinicIndex.md) |
| Semantic Blueprint | Layer-based symbolic reasoning & semantic modulations | [View →](https://github.com/onestardao/WFGY/tree/main/SemanticBlueprint/README.md) |
| Benchmark vs GPT-5 | Stress test GPT-5 with full WFGY reasoning suite | [View →](https://github.com/onestardao/WFGY/tree/main/benchmarks/benchmark-vs-gpt5/README.md) |
| 🧙♂️ Starter Village 🏡 | New here? Lost in symbols? Click here and let the wizard guide you through | [Start →](https://github.com/onestardao/WFGY/blob/main/StarterVillage/README.md) |
---
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