Retrieval Augmented Generation

Learn how to prioritize weak responses, streamline human-in-the-loop review, and use feedback to iteratively improve your retrieval, prompts, and models.

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.

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.