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.

🔗 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

Layer	Page	What it’s for
Proof	WFGY Recognition Map	External citations, integrations, and ecosystem proof
Engine	WFGY 1.0	Original PDF based tension engine
Engine	WFGY 2.0	Production tension kernel and math engine for RAG and agents
Engine	WFGY 3.0	TXT based Singularity tension engine, 131 S class set
Map	Problem Map 1.0	Flagship 16 problem RAG failure checklist and fix map
Map	Problem Map 2.0	RAG focused recovery pipeline
Map	Problem Map 3.0	Global Debug Card, image as a debug protocol layer
Map	Semantic Clinic	Symptom to family to exact fix
Map	Grandma’s Clinic	Plain language stories mapped to Problem Map 1.0
Onboarding	Starter Village	Guided tour for newcomers
App	TXT OS	TXT semantic OS, fast boot
App	Blah Blah Blah	Abstract and paradox Q and A built on TXT OS
App	Blur Blur Blur	Text to image with semantic control
App	Blow Blow Blow	Reasoning game engine and memory demo

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