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 …

## Introduction

AI agents are moving beyond simply generating text. Modern AI agents can interact with external systems, retrieve information, call APIs, query databases, execute functions, send messages, and perform actions on behalf of users.

This capability is commonly enabled through tool calling or function calling.

For example, an AI travel …

## Introduction

Generative AI has transformed the way we create and interact with information. Large Language Models (LLMs) can generate text, write code, summarize documents, answer questions, and assist with complex tasks within seconds.

However, these systems sometimes produce information that sounds convincing but is actually incorrect, misleading, or completely fabricated …