Sameer Gupta

Large‑language models (LLMs) already draft contracts, triage medical claims, and screen résumés. Every one of those tasks is laced with ethical choices, yet most benchmarks still judge models on math puzzles or multiple‑choice trivia. The authors of the paper “Auditing the Ethical Logic of Generative AI Models” step

Introduction In the realm of AI, especially with large language models, the prevailing belief is that complex reasoning tasks require detailed, step-by-step "thinking" processes. These processes often involve generating extensive intermediate steps before arriving at a solution, consuming significant computational resources. However, the paper titled "Reasoning Models

Prompt engineering might just be one of the most accessible yet impactful skills in artificial intelligence today. At its core, prompt engineering involves crafting specific inputs that guide Large Language Models (LLMs) to produce precise and accurate outputs, essential for ensuring AI quality. You don't have to be

Intro: Why this matters Picture this: You’re building an AI agent to help users book travel, troubleshoot software, or manage finances. One task leads to another. The user changes their mind halfway. New context unfolds mid-conversation. How do you train your AI to navigate all of that without losing

Imagine asking an AI, “What’s the best route to work tomorrow morning?” and receiving a response that not only understands your intent but actually checks live traffic data via an external API and gives you the optimal route. This isn’t a future concept. It’s happening right now—

This is Part 2 of our Agent Evaluations series. Here are Part 1 and Part 3 in this series. As AI agents start to gain traction across industries, driving innovation in tasks ranging from customer support to automation of tasks like booking requires their real-world performance evaluation to go beyond