The Silent Bottleneck in AI: The Interface Problem

At Deccan AI, quality isn’t a step-it’s the system. We deliver reliable, model-ready data through precision, consistency, and expert-driven workflows.

Inter-Rater Reliability

Explore why consistency among human annotators (IRR) is vital when training AI. This post delves into key metrics-Cohen’s Kappa, Krippendorff’s Alpha, ICC-and how they bolster data quality.

RLHF Introduction

A detailed breakdown of RLHF-how human preferences create reward models that steer LLMs toward safer, more aligned behavior. It covers the SFT → reward modeling → RL pipeline and real-world challenges.

The Human Touch in AI

Examines how human‑in‑the‑loop (HITL) systems and labeled data improve model safety, fairness, and edge‑case handling. Highlights the role of IRR in iterative refinement and ethical AI deployment.