Research / AI evaluation
AI Chat Benchmark
A 450-question benchmark that turns AI chat tuning from intuition into a comparable process.
01Questions→02Answers→03Scoring→04Iteration
Context
A retrieval, classification or prompt change may sound better while losing accuracy or adding latency. Without a stable test set, iterations are difficult to compare.
How it works
Built scenarios ranging from direct requests to contextual follow-ups, defined correctness criteria and separated answer quality from response-time measurement. Major iterations are compared through one consistent structure.
What it enables
The benchmark gives the team a decision baseline: it shows which change improved chat behavior, where a regression appeared and which part of the pipeline needs the next iteration.