Google Vertex AI: Guardrails and Fix Patterns

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

Visual map and recovery: RAG Architecture & Recovery
End-to-end retrieval knobs: Retrieval Playbook
Why this snippet: Retrieval Traceability
Embedding vs meaning: Embedding ≠ Semantic
Hallucination and chunk boundaries: Hallucination
Long chains and entropy: Context Drift, Entropy Collapse
Logic repair: Logic Collapse
Ordering control: Rerankers
Snippet and citation schema: Data Contracts
Multi-agent issues: Multi-Agent Problems

Fix in 60 seconds

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

Put citation-first headers in the system prompt.
Lock snippet schema via Retrieval Traceability and Data Contracts.
Add BBCR bridge for safety-neutral framing.
Add BBPF alternate path for tool calls with explicit timeouts.
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

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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