AI models are becoming increasingly capable of generating text, writing code, answering questions, and solving complex problems. But how do we know whether their outputs are actually good?

Traditionally, humans reviewed AI responses to measure their quality. As AI applications grow, manually checking every response becomes expensive and time-consuming. This …

An AI model can generate fluent, convincing answers and still be wrong. That's why building reliable AI applications requires more than choosing a powerful model — it requires a systematic way to measure how well that model performs.

AI evaluation helps engineers understand whether a model produces accurate, relevant, safe, and …

Why GenAI?

Generative AI has completely changed how I think about software, creativity, and problem-solving. Over the past year, I've gone deep into the world of large language models, prompt engineering, retrieval-augmented generation, fine-tuning, and AI agents. The pace of change is incredible, and I wanted a place to document …