WFGY/ProblemMap/GlobalFixMap/Eval/eval_cost_reporting.md

Eval: Cost Reporting and Efficiency

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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.

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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.

Open these first

• Latency vs Accuracy trade-off: eval_latency_vs_accuracy.md
• Benchmark suite: eval_benchmarking.md
• Observability probes: alerting_and_probes.md

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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

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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)

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JSON schema

{
  "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.

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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.

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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.

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🔗 Quick-Start Downloads (60 sec)

Tool	Link	3-Step Setup
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TXT OS (plain-text OS)	TXTOS.txt	1️⃣ Download · 2️⃣ Paste into any LLM chat · 3️⃣ Type “hello world” — OS boots instantly

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Explore More

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⭐ 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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