How our judge system picks the best AI answer

When you ask Simpledot a question, dozens of models answer in parallel. Showing you all of them would just move the comparison burden onto you — so we built a judge system that does the evaluation automatically, in seconds.

Scoring, not vibes

The judge evaluates each candidate response across several dimensions: factual accuracy, completeness, relevance to the actual question asked, clarity of explanation, and — for code — whether it would actually run. Each dimension is scored independently, and the weighted total decides the winner.

Crucially, the judge never knows which model produced which answer. Responses are anonymized before evaluation, so there is no brand bias: a smaller open model can and regularly does beat a famous one on specific tasks.

Task-aware weighting

A great answer to a coding question looks very different from a great answer to a strategy question. The judge detects the task type first, then adjusts its weights: correctness dominates for code and math, nuance and sourcing matter more for research, and tone matters for writing tasks.

The outcome is a single best answer with a confidence score — and if you're curious, you can always expand the run to see how the field performed. Transparency is a feature, not an afterthought.