Daniel Warfield

As context windows grow, some question RAG’s relevance, but its efficiency and targeted retrieval still solve problems full prompts can’t.

A breakdown of what Apple’s “Illusion of Thinking” paper reveals about the limits of reasoning models and the debate over what reasoning in AI actually means.

A breakdown of when to use MCP vs A2A for connecting agents to tools or each other.

RAG retrieves what matters, CAG remembers it. Together, they unlock faster, smarter enterprise AI.

The world’s most valuable data lives behind firewalls, not in the cloud. This is how to build a secure, high-performance RAG system on premises.

The world’s most important information doesn’t live on the public Internet and never will. This is how to set up private RAG in RedShift.

GroundX outperforms humans on the DocBench dataset

Evaluating RAG is hard. We peel back the onion on the major challenges and solutions.

Research shows vector databases lose accuracy at just 10,000 pages, but there's a way out.

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