Design intelligent AI agents with retrieval-augmented generation, memory components, and graph-based context integration.

Qdrant's $50M Series B and version 1.17 release make the case that agentic AI didn't simplify vector search — it scaled the ...

Several years ago, my linguistic research team and I began developing a computational tool we call "Read-y Grammarian." Our ...

Amazon Web Services has introduced Strands Labs, a new GitHub organization created to host experimental projects related to agent-based AI development.

Large Language Models (LLMs) have transformed natural language processing, but their limitations, such as fixed training data and lack of real-time updates, pose challenges for certain applications.

Databricks has released KARL, an RL-trained RAG agent that it says handles all six enterprise search categories at 33% lower ...

Despite widespread industry recommendations, a new ETH Zurich paper concludes that AGENTS.md files may often hinder AI coding agents. The researchers recommend omitting LLM-generated context files ...

Databricks' KARL agent uses reinforcement learning to generalize across six enterprise search behaviors — the problem that breaks most RAG pipelines.

This is a learner-focused project where you'll build a complete research assistant system that automatically fetches academic papers, understands their content, and answers your research questions ...

Bottom line: You can build a working RAG chatbot on Azure UK South in a single day using Azure AI Foundry's guided setup. The three services you need are Azure AI Search (retrieval), Azure OpenAI ...