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Eval: Operator Guidelines
This page sets strict rules for human operators and evaluators running WFGY-aligned evaluation suites. Evaluation is not a free-form activity. It must follow consistent contracts, logging standards, and reproducible steps.
Open these first
- Gold set design: goldset_curation.md
- Precision/recall evals: eval_rag_precision_recall.md
- Observability probes: alerting_and_probes.md
Acceptance targets
- Inter-annotator agreement ≥ 0.85 across 3 operators
- Annotation variance ≤ 10%
- Audit reproducibility ≥ 95% (rerun matches logged label)
- No operator-induced drift: ΔS stability remains within ±0.05 after evaluation
Operator responsibilities
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Stay schema-locked Every operator must apply the same definition of "correct" → answer cites the correct snippet and explains consistently.
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Use three-paraphrase rule Rephrase the query 3× before marking coverage. If answer flips across paraphrases, log as unstable.
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Log ΔS and λ Each annotated run must include structural metrics: ΔS(question,retrieved), λ states, snippet IDs.
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Apply cost view Record token counts and cost-per-correct when judging pipelines.
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No ad-hoc overrides Do not improvise “fixes” in evaluation logs. All fixes must point to an existing ProblemMap page.
Annotation protocol
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Step 1: Load baseline Open gold set. Verify expected snippet IDs.
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Step 2: Apply candidate system Run the same queries with candidate pipeline.
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Step 3: Compare
- If candidate cites correct snippet and stays under ΔS ≤ 0.45 → mark correct.
- If citation missing or ΔS ≥ 0.60 → mark fail, attach probable ProblemMap fix.
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Step 4: Log JSON
{
"operator": "user123",
"q_id": "Q42",
"question": "What is the cutoff for semantic drift?",
"expected_snippet": "S123",
"candidate_snippet": "S123",
"ΔS": 0.39,
"λ_state": "→",
"correct": true,
"cost_usd": 0.0012,
"notes": "Stable across 3 paraphrases"
}
When to escalate
- Annotators disagree by >15% → escalate to eval lead.
- ΔS drifts beyond thresholds but operators label "correct" → escalate and reconcile.
- Costs exceed 1.5× baseline → halt run until reviewed.
Audit checklist
- All logs stored in versioned JSONL.
- At least 2 operators per eval run.
- Spot check 10% of logs with independent reviewer.
- Archive outputs with system hash, gold set version, and ProblemMap pointers.
🔗 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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