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.

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

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:

Raw tokens
- input, output, total per query
- broken down by retrieval, rerank, reasoning
Cost per unit
- $/1k tokens per provider and model
- normalized into usd_equiv
Cost per correct
- (total spend ÷ number of correct answers)
- stratified by question bucket (short, medium, long)

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.

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.

Minimal run

Select 20 mixed-length questions.
Run baseline and candidate arms.
Compute cost per correct.
Ship only if candidate ≤ 1.3× baseline and stable across seeds.

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Tool	Link	3-Step Setup
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🧭 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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