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As AI continues to evolve, two technologies are converging to create a powerful new approach: Agentic RAG. Agentic RAG combines techniques from Retrieval Augmented Generation (RAG) with AI Agents (semi-autonomous AI) to push the boundaries of AI problem-solving.

Understanding and optimizing your parsing strategy is one of the keys to building high-performance RAG applications. There are several popular parsing strategies and tools out there, each with their own strengths and limitations.

Multimodal Retrieval-Augmented Generation (RAG) has emerged as a unique approach to increase efficiency and reliability of AI systems. This concept extends traditional text-based RAG systems to incorporate various data types such as images, audio, and video, creating richer and more contextually accurate information retrieval and generation.

CRAG, or the Comprehensive RAG Benchmark, is Meta’s newest benchmark to evaluate AI performance. We break the latest benchmark down and evaluate its importance for AI engineers.

Winner achieved 98% accuracy across 1,000+ pages of complex documents