# Eval: Cost Reporting and Efficiency
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> - [**Eval** — model evaluation and benchmarking](./README.md)
> - [**WFGY Global Fix Map** — main Emergency Room, 300+ structured fixes](../README.md)
> - [**WFGY Problem Map 1.0** — 16 reproducible failure modes](../../README.md)
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> **Evaluation disclaimer (cost reporting)**
> Any cost and efficiency numbers on this page come from specific runs with specific models and hardware.
> They are for comparison inside that context only and are not economic guarantees or universal prices.
---
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.
## Open these first
* 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)
* Benchmark suite: [eval\_benchmarking.md](https://github.com/onestardao/WFGY/blob/main/ProblemMap/GlobalFixMap/Eval/eval_benchmarking.md)
* Observability probes: [alerting\_and\_probes.md](https://github.com/onestardao/WFGY/blob/main/ProblemMap/GlobalFixMap/Eval_Observability/alerting_and_probes.md)
---
## Acceptance targets
* **Cost per correct answer** ≤ 1.3× baseline
* **Cost stability variance** ≤ 15% across 3 seeds and 3 paraphrases
* **Token efficiency** ≥ 0.7 (fraction of tokens contributing to correct citation)
* **Budget alerting**: auto-flag when projected monthly spend > 110% of budget cap
---
## Reporting dimensions
Each evaluation run must record cost on three levels:
1. **Raw tokens**
* input, output, total per query
* broken down by retrieval, rerank, reasoning
2. **Cost per unit**
* \$/1k tokens per provider and model
* normalized into `usd_equiv`
3. **Cost per correct**
* (total spend ÷ number of correct answers)
* stratified by question bucket (short, medium, long)
---
## JSON schema
```json
{
"suite": "v1_cost",
"arm": "with_hybrid",
"provider": "anthropic",
"model": "claude-3.7-sonnet",
"bucket": "long",
"precision": 0.79,
"recall": 0.68,
"ΔS_avg": 0.41,
"correct_answers": 40,
"total_questions": 50,
"tokens": { "in": 2850, "out": 920, "total": 3770 },
"cost_per_1k_tokens_usd": 0.006,
"spend_usd": 0.0226,
"cost_per_correct": 0.00056,
"variance_across_runs": 0.11,
"notes": "within budget and stable"
}
```
---
## Diagnostic questions
* Are rerankers worth the extra spend? → check ΔS reduction vs token increase.
* Is hybrid retrieval doubling retrieval tokens with little gain?
* Does the large model add accuracy, or is a small model + WFGY equal at lower cost?
* Is citation length inflated (long snippets)? → enforce snippet contract.
---
## Escalation and fixes
* **High cost per correct** → switch to caching, smaller model with WFGY overlay.
* **Variance >15%** → clamp paraphrases, normalize prompt headers.
* **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).
---
## Minimal run
1. Select 20 mixed-length questions.
2. Run baseline and candidate arms.
3. Compute cost per correct.
4. Ship only if candidate ≤ 1.3× baseline and stable across seeds.
---
### 🔗 Quick-Start Downloads (60 sec)
| Tool | Link | 3-Step Setup |
| -------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------ | ---------------------------------------------------------------------------------------- |
| **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 + \” |
| **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 |
---
### 🧭 Explore More
| Module | Description | Link |
| ------------------------ | ---------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------- |
| 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) |
| Problem Map 1.0 | Initial 16-mode diagnostic and symbolic fix framework | [View →](https://github.com/onestardao/WFGY/tree/main/ProblemMap/README.md) |
| 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) |
| Semantic Clinic Index | Expanded failure catalog: prompt injection, memory bugs, logic drift | [View →](https://github.com/onestardao/WFGY/blob/main/ProblemMap/SemanticClinicIndex.md) |
| Semantic Blueprint | Layer-based symbolic reasoning & semantic modulations | [View →](https://github.com/onestardao/WFGY/tree/main/SemanticBlueprint/README.md) |
| 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) |
| 🧙♂️ 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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