Category
AI Scoring & Evals
Benchmarks explained, evaluation frameworks, model testing
// 12 articles filed
// FIG. CAT-AI-E
PristrenPristrenBenchmarks explained, evaluation frameworks, model testing
// 12 articles filed
Neither informal testing nor published benchmarks alone can tell you whether a model is right for your use case. The right process uses both, in a specific order.
Mahmudul Haque Qudrati
CEO & ML Engineer
A/B testing LLM changes in production is how you confirm that a new model or prompt actually improves business outcomes. Here is the setup, what to measure, and the common mistakes that invalidate results.
Mahmudul Haque Qudrati
CEO & ML Engineer
RAGAS gives you four metrics that cover every major failure mode in a retrieval-augmented generation pipeline. Here is what each metric measures and how to act on low scores.
Mahmudul Haque Qudrati
CEO & ML Engineer
Chatbot Arena ranks LLMs through millions of real user preference votes rather than fixed benchmarks. It is the most contamination-resistant ranking system that exists today.
Mahmudul Haque Qudrati
CEO & ML Engineer
Red teaming is adversarial testing designed to find safety, reliability, and robustness failures in LLM applications before they reach production. Here is how to run a systematic red team exercise.
Mahmudul Haque Qudrati
CEO & ML Engineer
How to build an eval system that catches 80% of regressions with 20% of the effort. Start with real production examples, define clear scoring, and track it over time.
Mahmudul Haque Qudrati
CEO & ML Engineer
TruthfulQA measures whether models give truthful answers to questions humans often get wrong due to misconceptions. Its key finding — larger models can be more convincingly wrong — has real implications for high-stakes use cases.
Mahmudul Haque Qudrati
CEO & ML Engineer
A plain-English explanation of every major LLM benchmark: what each one tests, how it scores, and what a 1% difference actually means in practice.
Mahmudul Haque Qudrati
CEO & ML Engineer
SWE-Bench uses real GitHub issues from real projects to test whether models can write code that actually fixes software bugs. It is far more demanding than HumanEval.
Mahmudul Haque Qudrati
CEO & ML Engineer
Benchmarks are gamed and vibes do not scale. Here is how to build real evaluations that tell you whether an LLM actually works for your specific use case.
Mahmudul Haque Qudrati
CEO & ML Engineer
LM-as-judge works well for relative preference ranking but breaks down for absolute quality scores. Here is how to set it up and avoid the major failure modes.
Mahmudul Haque Qudrati
CEO & ML Engineer
Precision, recall, and F1 are the foundation of retrieval evaluation. Understanding the tradeoff between them tells you whether to optimize your RAG system for fewer wrong answers or fewer missed answers.
Mahmudul Haque Qudrati
CEO & ML Engineer
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