Jan: Guardrails and Fix Patterns
March 6, 2026 · View on GitHub
🧭 Quick Return to Map
You are in a sub-page of LocalDeploy_Inference.
To reorient, go back here:
- LocalDeploy_Inference — on-prem deployment and model inference
- WFGY Global Fix Map — main Emergency Room, 300+ structured fixes
- WFGY Problem Map 1.0 — 16 reproducible failure modes
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.
Jan is a desktop-native inference environment that allows you to run local LLMs with a polished UI, cross-platform support, and tight integration with quantized model formats. While easier to use than CLI runtimes, Jan inherits common problems: unstable context handling, schema drift, citation loss, and device-specific crashes. This page gives WFGY-based fixes to stabilize Jan deployments.
Open these first
- Architecture recovery: RAG Architecture & Recovery
- Retrieval knobs: retrieval-playbook.md
- Embedding sanity: embedding-vs-semantic.md
- Context stability: context-drift.md, entropy-collapse.md
- Safety and schema: prompt-injection.md, logic-collapse.md
- Boot/deploy: bootstrap-ordering.md, predeploy-collapse.md
Core acceptance
- ΔS(question, retrieved) ≤ 0.45
- Coverage ≥ 0.70 for the target section
- λ convergent across 3 paraphrases × 2 seeds
- JSON schema locked for tool calls
- Observability of ΔS and λ logged per run
Common Jan breakpoints
| Symptom | Likely Cause | Fix |
|---|---|---|
| First run fails on GPU device | CUDA/Metal init order | bootstrap-ordering.md |
| Correct snippets but drifting answers | Schema mismatch in local context buffer | retrieval-traceability.md, data-contracts.md |
| Answers alternate between runs | λ flip, unstable headers | context-drift.md |
| JSON parse breaks | Inconsistent serialization in UI layer | logic-collapse.md |
| Safety refusal hides citations | Missing citation-first prompting | retrieval-traceability.md |
Fix in 60 seconds
- Run warm-up: issue a small dummy query to stabilize device kernels.
- Schema enforce: lock JSON outputs for tools and citations.
- Trace citations: enforce cite-then-explain.
- Measure ΔS and λ: if ΔS ≥ 0.60, rebuild index with proper embedding metric.
- Watch entropy: reset conversation memory after 4k–8k tokens or entropy rise.
Diagnostic prompt (copy-paste)
I am using Jan to run a local GGUF/GGML model.
Question: "{user_question}"
Return:
- ΔS(question, retrieved)
- λ across paraphrases and seeds
- JSON schema compliance
- Which WFGY fix page to open if ΔS ≥ 0.60
🔗 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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