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Google AI (Gemini): Guardrails and Fix Patterns

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A compact field guide to stabilize Gemini calls inside RAG, agents, or long workflows. Use the checks below to localize failure, then jump to the exact WFGY fix page.

Core acceptance

  • ΔS(question, retrieved) ≤ 0.45
  • Coverage ≥ 0.70 for the target section
  • λ remains convergent across 3 paraphrases
  • E_resonance stable on long windows

Open these first


Typical breakpoints and the right fix

Symptom you see Likely cause Fix page
High similarity yet wrong meaning Metric or index mismatch Embedding ≠ Semantic
Gemini cites the wrong paragraph Chunk boundaries and trace loss Hallucination, Retrieval Traceability
Answers flip across runs λ instability on long threads Context Drift, Entropy Collapse
Refuses or loops on safe content Prompt contract not locked Data Contracts
Good recall but bad ordering Reranking missing Rerankers
Corrected errors reappear Re-entry without variance clamp pattern_hallucination_reentry.md

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.
  1. Probe with λ_observe
  • Vary k ∈ {5, 10, 20}. Flat high curve means metric or index mismatch.
  • Reorder prompt headers. If ΔS spikes, lock the schema.
  1. Apply the module
  • Retrieval drift → BBMC + Data Contracts.
  • Reasoning collapse → BBCR bridge + BBAM variance clamp.
  • Dead ends in long runs → BBPF alternate path with explicit step limits.
  1. Verify
  • Coverage ≥ 0.70 on the target section.
  • Three paraphrases keep ΔS ≤ 0.45 and λ convergent.
  • Re-run with seed change and shuffled snippet order.

Gemini-specific gotchas

  • Tool and JSON calls
    If the function schema is loose, Gemini may hallucinate fields. Lock schemas with Data Contracts and clamp variance with BBAM.

  • Safety flips on neutral text
    When the role block is not pinned, safety can overfire. Use a citation-first header from Retrieval Traceability and keep source boundaries explicit.

  • Hybrid retrieval regressions
    HyDE plus keyword can split queries. Check pattern_query_parsing_split.md and add a stable anchor paragraph to reduce drift.

  • Long context smear
    Large windows flatten meaning if chunks are not semantic. Rebuild with the chunking checklist and verify joins with ΔS probes.


Copy-paste prompt (safe)


read the WFGY TXT OS and Problem Map pages. extract ΔS, λ\_observe, E\_resonance and modules BBMC, BBPF, BBCR, BBAM.
given my gemini failure:

* symptom: \[brief]
* traces: \[ΔS(question, retrieved)=…, ΔS(retrieved, anchor)=…, λ states]

tell me:

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


Escalation path


🔗 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
⭐ Proof WFGY Recognition Map External citations, integrations, and ecosystem proof
⚙️ Engine WFGY 1.0 Original PDF tension engine and early logic sketch (legacy reference)
⚙️ Engine WFGY 2.0 Production tension kernel for RAG and agent systems
⚙️ Engine WFGY 3.0 TXT based Singularity tension engine (131 S class set)
🗺️ Map Problem Map 1.0 Flagship 16 problem RAG failure taxonomy and fix map
🗺️ Map Problem Map 2.0 Global Debug Card for RAG and agent pipeline diagnosis
🗺️ Map Problem Map 3.0 Global AI troubleshooting atlas and failure pattern map
🧰 App TXT OS .txt semantic OS with fast bootstrap
🧰 App Blah Blah Blah Abstract and paradox Q&A built on TXT OS
🧰 App Blur Blur Blur Text to image generation with semantic control
🏡 Onboarding Starter Village Guided entry point for new users

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