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ProblemMap/GlobalFixMap/Eval/eval_cost_reporting.md
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# Eval: Cost Reporting and Efficiency
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This page defines how to measure and report **cost per correct answer** in retrieval-augmented and reasoning pipelines. Latency and accuracy alone are insufficient. Without cost analysis, systems regress into wasteful configurations.
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## Open these first
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* Latency vs Accuracy trade-off: [eval\_latency\_vs\_accuracy.md](https://github.com/onestardao/WFGY/blob/main/ProblemMap/GlobalFixMap/Eval/eval_latency_vs_accuracy.md)
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* Benchmark suite: [eval\_benchmarking.md](https://github.com/onestardao/WFGY/blob/main/ProblemMap/GlobalFixMap/Eval/eval_benchmarking.md)
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* Observability probes: [alerting\_and\_probes.md](https://github.com/onestardao/WFGY/blob/main/ProblemMap/GlobalFixMap/Eval_Observability/alerting_and_probes.md)
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---
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## Acceptance targets
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* **Cost per correct answer** ≤ 1.3× baseline
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* **Cost stability variance** ≤ 15% across 3 seeds and 3 paraphrases
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* **Token efficiency** ≥ 0.7 (fraction of tokens contributing to correct citation)
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* **Budget alerting**: auto-flag when projected monthly spend > 110% of budget cap
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---
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## Reporting dimensions
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Each evaluation run must record cost on three levels:
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1. **Raw tokens**
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* input, output, total per query
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* broken down by retrieval, rerank, reasoning
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2. **Cost per unit**
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* \$/1k tokens per provider and model
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* normalized into `usd_equiv`
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3. **Cost per correct**
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* (total spend ÷ number of correct answers)
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* stratified by question bucket (short, medium, long)
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---
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## JSON schema
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```json
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{
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"suite": "v1_cost",
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"arm": "with_hybrid",
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"provider": "anthropic",
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"model": "claude-3.7-sonnet",
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"bucket": "long",
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"precision": 0.79,
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"recall": 0.68,
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"ΔS_avg": 0.41,
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"correct_answers": 40,
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"total_questions": 50,
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"tokens": { "in": 2850, "out": 920, "total": 3770 },
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"cost_per_1k_tokens_usd": 0.006,
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"spend_usd": 0.0226,
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"cost_per_correct": 0.00056,
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"variance_across_runs": 0.11,
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"notes": "within budget and stable"
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}
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```
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---
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## Diagnostic questions
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* Are rerankers worth the extra spend? → check ΔS reduction vs token increase.
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* Is hybrid retrieval doubling retrieval tokens with little gain?
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* Does the large model add accuracy, or is a small model + WFGY equal at lower cost?
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* Is citation length inflated (long snippets)? → enforce snippet contract.
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---
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## Escalation and fixes
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* **High cost per correct** → switch to caching, smaller model with WFGY overlay.
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* **Variance >15%** → clamp paraphrases, normalize prompt headers.
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* **Budget overrun** → auto-throttle evals, alert with [alerting\_and\_probes.md](https://github.com/onestardao/WFGY/blob/main/ProblemMap/GlobalFixMap/Eval_Observability/alerting_and_probes.md).
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---
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## Minimal run
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1. Select 20 mixed-length questions.
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2. Run baseline and candidate arms.
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3. Compute cost per correct.
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4. Ship only if candidate ≤ 1.3× baseline and stable across seeds.
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---
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### 🔗 Quick-Start Downloads (60 sec)
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| Tool | Link | 3-Step Setup |
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| -------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------ | ---------------------------------------------------------------------------------------- |
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| **WFGY 1.0 PDF** | [Engine Paper](https://github.com/onestardao/WFGY/blob/main/I_am_not_lizardman/WFGY_All_Principles_Return_to_One_v1.0_PSBigBig_Public.pdf) | 1️⃣ Download · 2️⃣ Upload to your LLM · 3️⃣ Ask “Answer using WFGY + \<your question>” |
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| **TXT OS (plain-text OS)** | [TXTOS.txt](https://github.com/onestardao/WFGY/blob/main/OS/TXTOS.txt) | 1️⃣ Download · 2️⃣ Paste into any LLM chat · 3️⃣ Type “hello world” — OS boots instantly |
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---
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### 🧭 Explore More
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| Module | Description | Link |
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| ------------------------ | ---------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------- |
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| WFGY Core | WFGY 2.0 engine is live: full symbolic reasoning architecture and math stack | [View →](https://github.com/onestardao/WFGY/tree/main/core/README.md) |
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| Problem Map 1.0 | Initial 16-mode diagnostic and symbolic fix framework | [View →](https://github.com/onestardao/WFGY/tree/main/ProblemMap/README.md) |
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| Problem Map 2.0 | RAG-focused failure tree, modular fixes, and pipelines | [View →](https://github.com/onestardao/WFGY/blob/main/ProblemMap/rag-architecture-and-recovery.md) |
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| Semantic Clinic Index | Expanded failure catalog: prompt injection, memory bugs, logic drift | [View →](https://github.com/onestardao/WFGY/blob/main/ProblemMap/SemanticClinicIndex.md) |
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| Semantic Blueprint | Layer-based symbolic reasoning & semantic modulations | [View →](https://github.com/onestardao/WFGY/tree/main/SemanticBlueprint/README.md) |
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| Benchmark vs GPT-5 | Stress test GPT-5 with full WFGY reasoning suite | [View →](https://github.com/onestardao/WFGY/tree/main/benchmarks/benchmark-vs-gpt5/README.md) |
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| 🧙♂️ Starter Village 🏡 | New here? Lost in symbols? Click here and let the wizard guide you through | [Start →](https://github.com/onestardao/WFGY/blob/main/StarterVillage/README.md) |
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---
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> 👑 **Early Stargazers: [See the Hall of Fame](https://github.com/onestardao/WFGY/tree/main/stargazers)** —
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> Engineers, hackers, and open source builders who supported WFGY from day one.
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> <img src="https://img.shields.io/github/stars/onestardao/WFGY?style=social" alt="GitHub stars"> ⭐ [WFGY Engine 2.0](https://github.com/onestardao/WFGY/blob/main/core/README.md) is already unlocked. ⭐ Star the repo to help others discover it and unlock more on the [Unlock Board](https://github.com/onestardao/WFGY/blob/main/STAR_UNLOCKS.md).
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<div align="center">
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[](https://github.com/onestardao/WFGY)
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[](https://github.com/onestardao/WFGY/tree/main/OS)
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[](https://github.com/onestardao/WFGY/tree/main/OS/BlahBlahBlah)
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[](https://github.com/onestardao/WFGY/tree/main/OS/BlotBlotBlot)
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[](https://github.com/onestardao/WFGY/tree/main/OS/BlocBlocBloc)
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[](https://github.com/onestardao/WFGY/tree/main/OS/BlurBlurBlur)
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[](https://github.com/onestardao/WFGY/tree/main/OS/BlowBlowBlow)
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</div>
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