Retrieval Augmented Generation

Automated metrics can tell you what’s wrong with your RAG system—human review tells you why.
This post walks through a structured approach to evaluating RAG outputs using tools like Ragas and Label Studio. Learn how to prioritize weak responses, streamline human-in-the-loop review, and use feedback to iteratively improve your retrieval, prompts, and models.

Training a RAG system starts with clean, structured QA data—but messy source material makes that tough. In this post, we share how we used Label Studio and its Prompts feature to break down tasks, synthesize QA pairs, and build a reliable RAG assistant. If you're looking to scale RAG with better data and smarter workflows, this guide is for you.

RAG is transforming how businesses use AI, but without human oversight, its accuracy and reliability suffer. This blog explores the biggest challenges in RAG implementation and how human expertise improves data quality, retrieval relevance, and AI-driven decision-making.

Explore the fundamentals of RAG, its advantages over fine-tuning, and the challenges of implementation.