Quaerens AI Labs Vol. I · Inquiry as Method · MMXXVI
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QA Arena · controlled experiments
Operational

QA Arena tests QA-generation strategies before you scale them.

You have a QA-generation use case. Many strategies are possible: different evals, models, provider tools, source-enrichment methods, research ideas, and budgets. QA Arena turns those choices into controlled experiments and shows what is worth scaling.

See the worked example How evidence is controlled →
§ 01 From use case to tested strategy

A team does not start with a leaderboard. It starts with questions.

QA Arena turns those questions into small controlled experiments. The output is a decision:

scale retest change strategy stop
Q1
For this use case, which evals best measure quality, hallucination, grounding, and cost?
Q2
Will Distilabel perform better than Meta SDK or DeepEval on those evals?
Q3
Does a local model perform well enough, or do we need a frontier model?
Q4
If we enrich the source using a strategy from a research paper, does QA quality improve?
Q5
Can we generate enough good QAs before spending more budget?
§ 02 What QA Arena compares
Evaluation strategy

Which evals actually measure the failure modes that matter?

quality hallucination grounding answerability cost coverage
Generator / provider strategy

Which generator or provider performs better for this use case?

Distilabel Meta SDK DeepEval Synthetic Data Kit DataDreamer Kiln
Source strategy

Does source enrichment improve the result?

raw source summary-first clause map concept map research-inspired
Model / runtime strategy

What changes when generation runs locally, in the cloud, or through an agentic runtime?

local model frontier model open-source model agent coder
Budget strategy

Does more generation volume improve quality, or only produce more candidates?

tiny pilot larger batch repeated runs scale-up floor
Research strategy

Can an idea from a paper become a better QA-generation or evaluation method?

paper replay strategy adaptation controlled arm scale-up test
§ 03 Current worked example
Notebook diagram — QA envelope with six strategy levers around a fixed grounded-document-QA goal

Fig. 1 — Grounded Document QA, strategy choices under test

Grounded Document QA

One use case, fixed

Generate trustworthy QAs from source documents.

QA Arena then tests competing strategy choices around that use case:

Which evals measure grounded quality?
Which generation tools work better?
Which source-enrichment methods help?
Which model/runtime tradeoff is acceptable?
Which strategy is worth scaling?
Open the worked example Evidence stage Pilot
§ 04 What QA Arena does not do

QA Arena does not publish global winners.

It does not say

Distilabel is better than DeepEval.

This model is the best.

This enrichment method always wins.

It says

For this use case, under these conditions, this strategy produced stronger evidence.

No global winners. Scoped evidence only.

A result is valid inside its use case, source, strategy, evaluator, model, budget, and evidence stage.

Pilot ≠ proven High score ≠ robust evidence Failure is recorded evidence
Read the methodology →
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Evidence over claims · scoped over global
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