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OpenAI's o1 reasoning model: A step towards AI that thinks, promising improved rule-following and consistency, but not without skepticism and limitations.

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

Dive into the world of AI fine-tuning, from basic concepts to cutting-edge techniques like LoRA. Learn how to specialize your models, avoid common pitfalls, and leverage fine-tuning for real-world applications.

LLM routing is transforming AI system architecture by intelligently directing prompts to the most suitable language models, balancing quality, speed, and cost for optimal performance.

Learn how AskVet is using AI and Retrieval Augmented Generation (RAG) to revolutionize pet care. Discover their unique mood bucket and sentiment system and learn tips for developers to implement RAG and other AI solutions across industries like AI in veterinary services.

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