GoHighLevel (GHL) — Guardrails and Fix Patterns

This page is for workflows orchestrated inside GoHighLevel.
Use it when your RAG or agent flow runs through GHL Workflows, Webhooks, or Custom Actions and starts to misbehave.

Acceptance targets

ΔS(question, retrieved) ≤ 0.45
coverage ≥ 0.70 to the target section
λ stays convergent across 3 paraphrases

Typical breakpoints → exact fixes

Triggers fire before data or index is actually ready
Fix No.14: Bootstrap Ordering →
bootstrap-ordering.md
First call after deploy crashes or picks the wrong secret
Fix No.16: Pre-Deploy Collapse →
predeploy-collapse.md
Circular waits between CRM updates, webhooks, background jobs
Fix No.15: Deployment Deadlock →
deployment-deadlock.md
High cosine similarity but the meaning is off
Fix No.5: Embedding ≠ Semantic →
embedding-vs-semantic.md
Snippet feels unrelated or citations don’t match the source
Fix No.8: Retrieval Traceability →
retrieval-traceability.md
Contract the payload with Data Contracts →
data-contracts.md
Hybrid retrieval (external tools) does worse than a single retriever
Pattern: Query Parsing Split →
pattern_query_parsing_split.md
Also review Rerankers →
rerankers.md
Facts are indexed yet never show up
Pattern: Vectorstore Fragmentation →
pattern_vectorstore_fragmentation.md
Two sources get blended into one answer in long flows
Pattern: Symbolic Constraint Unlock (SCU) →
pattern_symbolic_constraint_unlock.md

Minimal GHL workflow checklist

Warm-up fence
Before any LLM step, ping a health endpoint that checks VECTOR_READY, INDEX_HASH, and secret_rev.
If not ready, delay or requeue. Spec lives in
bootstrap-ordering.md.
Idempotency
Build dedupe_key = sha256(contact_id + wf_rev + index_hash) in a Custom Action.
Store in KV or a custom field, drop duplicates.
RAG boundary contract
Always pass snippet_id, section_id, source_url, offsets, tokens.
Enforce cite then explain. Specs:
retrieval-traceability.md · data-contracts.md
Observability probes
Log ΔS(question, retrieved) and λ per stage. Alert on ΔS ≥ 0.60 or λ divergent.
Overview map:
RAG Architecture & Recovery
Single writer
Route CRM writes and external publishes through one writer branch with dedupe.
See: deployment-deadlock.md
Regression gate
Require coverage ≥ 0.70 and ΔS ≤ 0.45 before publish.
Eval spec:
eval_rag_precision_recall.md

Copy-paste prompt for the GHL LLM step


I uploaded TXT OS and the WFGY Problem Map files.
This GHL workflow retrieved {k} snippets with fields {snippet\_id, section\_id, source\_url, offsets}.
Question: "{user\_question}"

Do:

1. Enforce cite-then-explain. If any citation is missing, stop and return which fix page to open.
2. Compute ΔS(question, retrieved). If ΔS ≥ 0.60, point me to the minimal structural fix:
   retrieval-playbook, retrieval-traceability, data-contracts, rerankers.
3. Output compact JSON:
   { "citations": \[...], "answer": "...", "λ\_state": "→|←|<>|×", "ΔS": 0.xx, "next\_fix": "..." }

Common GHL gotchas

Connection switching between staging and prod.
Stamp env, INDEX_HASH, secret_rev in traces and block on mismatch.
Parallel branches touching the same contact or store.
Use a mutex or single writer, keep writes idempotent.
Webhook payload silently renames fields.
Validate against the data contract before the LLM.
External rate limits make hybrids unstable.
Prefer dense retriever plus reranking, keep params logged.

When to escalate

ΔS stays ≥ 0.60 after chunk and retrieval fixes → rebuild index with explicit metric and normalization.
See retrieval-playbook.md
Same input flips answers between runs → check version skew and memory desync.
See predeploy-collapse.md

🔗 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

🧭 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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