WFGY/ProblemMap/GlobalFixMap/Automation/retool.md

Retool — Guardrails and Fix Patterns

Use this when your stack uses Retool (Queries, Transformers, Workflows, Resources) and you see wrong snippets, unstable reasoning, mixed sources, or silent failures that look fine in logs.

Acceptance targets

• ΔS(question, retrieved) ≤ 0.45
• Coverage ≥ 0.70 to the intended section or record
• λ stays convergent across 3 paraphrases

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Typical breakpoints → exact fixes

• Query returns plausible but wrong rows or snippets
  Fix No.1: Hallucination and Chunk Drift →
  Hallucination
  Also review the Retrieval Playbook →
  Retrieval Playbook

• High cosine similarity but meaning is wrong
  Fix No.5: Embedding ≠ Semantic →
  Embedding ≠ Semantic

• Some facts exist but never surface in search widgets or tables
  Pattern: Vectorstore Fragmentation →
  Vectorstore Fragmentation

• Citations missing or inconsistent between retriever result and LLM response
  Fix No.8: Retrieval Traceability with Data Contracts →
  Retrieval Traceability · Data Contracts

• Long chains flatten tone or drift logically in Workflows
  Fix No.3 and No.9: Context Drift and Entropy Collapse →
  Context Drift · Entropy Collapse

• App works in Preview but breaks after deploy or after environment switch
  Fix No.16 and Infra family →
  Pre-Deploy Collapse · Bootstrap Ordering · Deployment Deadlock

• Confident tone but incorrect answers in AI components
  Fix No.4: Bluffing and Overconfidence →
  Bluffing

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Minimal Retool pattern with WFGY checks

// Retool App example: one LLM answer path with observable retrieval and WFGY checks

// 1) Retrieval query (REST or SQL). Keep params explicit and logged.
const k = 10;
const question = textInput_question.value;

// Example fetch to your retriever API
const retrieved = await retrieverApi.trigger({
  additionalScope: { question, k }   // ensure same tokenizer and metric across write/read
});

// 2) Assemble schema-locked prompt. Cite first, then explain.
const context = joinSnippets(retrieved.data);
const prompt = `
SYSTEM:
You must cite lines before any explanation.
TASK:
Answer the user's question using the provided context.
CONSTRAINTS:
- Do not mix sources
- Provide snippet_id for each citation
CONTEXT:
${context}
QUESTION:
${question}
`;

// 3) Call model
const answer = await llmApi.trigger({ additionalScope: { prompt }});

// 4) WFGY post-checks. Compute ΔS(question, context) and record trace table.
const metrics = await wfgyCheckApi.trigger({
  additionalScope: { question, context, answer: answer.data }
});

// 5) Fail fast when ΔS ≥ 0.60 or λ is divergent
if (metrics.data.deltaS >= 0.60 || metrics.data.lambda !== "→") {
  utils.showNotification("High semantic stress. See trace tab.", "warning");
  return { status: "needs_fix", ...metrics.data };
}

return { status: "ok", answer: answer.data, ...metrics.data };

What this enforces

• Retrieval is parameterized and observable in Retool Query logs.
• Prompt is schema locked with citation first.
• WFGY check runs after generation and can stop the run when ΔS is high or λ flips.
• Traces are kept as a snippet to citation table for audit.

Reference specs RAG Architecture and Recovery · Retrieval Playbook · Retrieval Traceability · Data Contracts

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Retool specific gotchas

• Resource points to a different environment or secret than the indexer used. Pin versions and verify headers. See Pre-Deploy Collapse

• Mixed metrics or normalization between ingestion code and query code in Workflows. Rebuild with explicit metric and unit normalization. See Embedding ≠ Semantic

• Transformers silently reshape or re-rank without trace. Require cite first and include snippet_id headers. See Retrieval Traceability and Data Contracts

• Parallel queries cause ordering instability. Add a rerank step only after per-source ΔS ≤ 0.50. See Rerankers

• Workflow scheduled runs build a fresh index incorrectly. Enforce idempotent builds with boot checks. See Bootstrap Ordering

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When to escalate

• ΔS stays ≥ 0.60 after chunk and retrieval fixes Work through the playbook and rebuild index parameters. Retrieval Playbook

• Answers flip between environments or sessions Verify version skew and session state. Pre-Deploy Collapse

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🔗 Quick-Start Downloads (60 sec)

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

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🧭 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 →
🧙‍♂️ Starter Village 🏡	New here? Lost in symbols? Click here and let the wizard guide you through	Start →

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