Context Engineering & AI - Context engineering is evolving from simple prompt engineering to a dynamic approach that feeds AI with wider context for better results [3] - Context engineering enables selective curation of information relevant to specific domains, especially important in enterprise environments [4] - Structuring input in context engineering improves signal over noise, addressing a major problem with current AI models [5] - Memory, both short-term and long-term, is crucial for AI, enabling collaboration, remembering conversation history, and effective long-term operations [10][11][12] Knowledge Graphs & Graph RAG - Knowledge graphs provide structured information that complements AI's ability to create and pull from different sources [17] - Graph RAG, which uses graphs as part of the retrieval process, provides more relevant results than vector similarity search by incorporating relationships, nodes, and community groupings [22][23] - Graph RAG enables explainable AI and allows for the implementation of role-based access control, ensuring that only authorized individuals can access specific information [25] Neo4j Solutions & Resources - Neo4j offers a knowledge graph builder, a web application that allows users to upload files and generate knowledge graphs [28] - Neo4j's MCP server is an open-source extension that enables querying knowledge graphs using Cypher, a graph query language [46] - Neo4j provides resources like Graph Academy (free learning resources) and Nodes AI (virtual conference) for learning about graph technology and AI applications [53][54]