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Google Vertex AI: Guardrails and Fix Patterns

🧭 Quick Return to Map

You are in a sub-page of LLM_Providers.
To reorient, go back here:

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.

A compact field guide for Gemini on Vertex AI. Use this page when failures look provider specific. The checks route you to the exact WFGY fix page and give a minimal recipe you can paste into your runbook.

Core acceptance

  • ΔS(question, retrieved) ≤ 0.45
  • coverage ≥ 0.70 for the target section
  • λ remains convergent across 3 paraphrases and 2 seeds

Open these first

Fix in 60 seconds

  1. Measure ΔS

    • Compute ΔS(question, retrieved) and ΔS(retrieved, expected anchor).
    • Thresholds: stable < 0.40, transitional 0.40–0.60, risk ≥ 0.60.

  2. Probe with λ_observe

    • Vary k = 5, 10, 20. If ΔS goes flat high, suspect index or metric mismatch.
    • Reorder prompt headers. If ΔS spikes, lock the schema.

  3. Apply the module

    • Retrieval drift → BBMC + Data Contracts.
    • Reasoning collapse → BBCR bridge + BBAM variance clamp.
    • Safety or tool-call stalls → BBPF alternate path with explicit timeouts.

Typical breakpoints and the right fix

  • Safety filters block or rewrite

    • Symptom: answer disappears or becomes generic; logs show blocked categories.
    • Fix path: keep the request task-bound and citation-first. Apply Data Contracts and a BBCR bridge that states lawful scope and cites sources. Verify with Retrieval Traceability.

  • Tool call returned “no function call” despite valid tools

    • Symptom: model narrates instead of calling the function; JSON keys omitted.
    • Fix path: lock the tool schema in the prompt header using Data Contracts. Add BBPF fallback branch that emits the same call with minimal args when λ flips.

  • Streaming truncation or partial JSON

    • Symptom: closing braces missing or content clipped.
    • Fix path: BBAM variance clamp on output length and a post-validator that re-asks only for the missing tail. If loops appear, follow Logic Collapse.

  • Hybrid retrieval worse than single retriever

    • Symptom: HyDE + BM25 underperform and top-k is noisy.
    • Fix path: apply the split pattern and retune as in Query Parsing Split. Then re-order with Rerankers.

  • Indexed facts never show up

    • Symptom: high recall offline, zero hits online.
    • Fix path: check fragmentation and rebuild per Vectorstore Fragmentation. Re-probe ΔS after rebuild.

  • Session flips between tabs or seeds

    • Symptom: same prompt, different claims by session.
    • Fix path: pin the instruction header, move citations above free text, and follow Memory Desync.

Provider-specific knobs to audit

  • Model pinning

    • Pin an exact Gemini version where possible. Note the tokenizer budget before you add long system headers.

  • Safety configuration

    • Keep scope lawful and narrow in the header. If the task is research or code reading, state that explicitly and cite sources. This avoids silent rewrites.

  • Tool schema shape

    • Function name, arg names, and enum values must match your declared schema. Enforce with Data Contracts and a post-validator.

  • Context budget

    • Large tool results or many citations can clip the tail. Trim with BBMC and move the schema above narrative text.

  • Region and project hygiene

    • Mismatched locations or stale projects can surface different defaults. Record the config in your trace header so λ checks are comparable.

Minimal recipe

  1. Put citation-first headers in the system prompt.
  2. Lock snippet schema via Retrieval Traceability and Data Contracts.
  3. Add BBCR bridge for safety-neutral framing.
  4. Add BBPF alternate path for tool calls with explicit timeouts.
  5. Verify acceptance using Eval Semantic Stability and RAG Precision/Recall.

Copy-paste prompt for your Vertex runbook


I am running on Google Vertex AI.
My failure:

* symptom: \[brief]
* traces: \[ΔS(question,retrieved)=..., ΔS(retrieved,anchor)=..., λ states, safety logs if any]

Tell me:

1. which layer is failing and why,
2. which exact WFGY fix page from this repo to open,
3. minimal steps to push ΔS ≤ 0.45 and keep λ convergent,
4. how to verify with a reproducible test.

Use BBMC/BBPF/BBCR/BBAM when relevant. If safety gating is suspected, propose a compliant BBCR rewrite and show the acceptance targets.

🔗 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

Layer Page What it’s for
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Engine WFGY 1.0 Original PDF based tension engine
Engine WFGY 2.0 Production tension kernel and math engine for RAG and agents
Engine WFGY 3.0 TXT based Singularity tension engine, 131 S class set
Map Problem Map 1.0 Flagship 16 problem RAG failure checklist and fix map
Map Problem Map 2.0 RAG focused recovery pipeline
Map Problem Map 3.0 Global Debug Card, image as a debug protocol layer
Map Semantic Clinic Symptom to family to exact fix
Map Grandma’s Clinic Plain language stories mapped to Problem Map 1.0
Onboarding Starter Village Guided tour for newcomers
App TXT OS TXT semantic OS, fast boot
App Blah Blah Blah Abstract and paradox Q and A built on TXT OS
App Blur Blur Blur Text to image with semantic control
App Blow Blow Blow Reasoning game engine and memory demo

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