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

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Whether you are looking for an LLM with more safety guardrails or one completely without them, someone has probably built it.

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 ...

Process Diverse Data Types at Scale: Through the Unstructured partnership, organizations can automatically parse and transform documents, PDFs, images, and audio into high-quality embeddings at ...

Agent skills shift AI agents toward procedural tasks with skill.md steps; progressive disclosure reduces context window bloat in real use.

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

AI in architecture is moving from experimentation to implementation. An AJ webinar supported by CMap explored how practices are applying these tools to live projects, construction delivery and operati ...

In this tutorial, we build an elastic vector database simulator that mirrors how modern RAG systems shard embeddings across distributed storage nodes. We implement consistent hashing with virtual ...

This repository contains the official implementation of our uncertainty-aware multimodal RAG framework for cleft lip and palate (CL/P) assessment. The system combines: . ├── README.md # This file ├── ...