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]

If you found it insightful, reshare it with your network.Find me → @_avichawlaEvery day, I share tutorials and insights on DS, ML, LLMs, and RAGs. ...

General Overview - The author encourages readers to reshare the content if they found it insightful [1] - The author shares tutorials and insights on Data Science (DS), Machine Learning (ML), Large Language Models (LLMs), and Retrieval-Augmented Generation (RAGs) daily [1] Author Information - The author can be found on Twitter/X with the handle @_avichawla [1]

Core Insights - The wealth management industry is undergoing a significant transformation driven by the rise of artificial intelligence (AI) and increasingly complex investor behavior [1][2][4] Group 1: Impact of AI on Wealth Management - AI will play a crucial role in enhancing advisor-client relationships by taking over tedious tasks such as tax planning, legal matters, and portfolio management, allowing advisors to focus more on client interactions [2][4] - The use of AI tools can help advisors and portfolio managers gain insights into market dynamics, including trending topics and sentiment analysis, which is essential for understanding market events [3][4] Group 2: Importance of Narrative Intelligence - Narrative intelligence is becoming a key differentiator, helping advisors interpret market sentiment and guide clients through emotional decision-making [4][7] - By leveraging sentiment analysis and natural language processing, advisors can help clients understand market events, reducing the likelihood of panic selling or irrational investment decisions [5][6] Group 3: Ensuring Trust in AI Tools - Trust in AI tools depends on transparency and multi-layered validation, with companies needing to adopt best practices to ensure the reliability and relevance of insights [4][6] - Practical measures include ensuring AI tools can trace information sources and employing prompt engineering to improve the quality of outputs from AI systems [6][7]

General Information - The author encourages readers to reshare the content if they found it insightful [1] - The author shares tutorials and insights on DS (Data Science), ML (Machine Learning), LLMs (Large Language Models), and RAGs (Retrieval-Augmented Generation) daily [1] Author Information - The author can be found on Twitter (now X) with the handle @_avichawla [1]

General Overview - The document is a wrap-up message encouraging readers to reshare the content if they found it insightful [1] - It promotes tutorials and insights on Data Science (DS), Machine Learning (ML), Large Language Models (LLMs), and Retrieval-Augmented Generation (RAGs) [1] Author Information - Avi Chawla (@_avichawla) shares daily tutorials and insights [1] Project Focus - The content focuses on generating a Large Language Model (LLM) fine-tuning dataset locally [1]

That's a wrap!If you found it insightful, reshare it with your network.Find me → @_avichawlaEvery day, I share tutorials and insights on DS, ML, LLMs, and RAGs.Avi Chawla (@_avichawla):There's a new way to build production-grade MCP servers.- It takes less than a minute.- You don't have to write any code.- You can integrate from 100k+ tools.Here's a step-by-step breakdown (100% local): ...

Agent Technology & Protocol - Agent2Agent (A2A) protocol is explained with visuals [1] - Tutorials and insights on DS, ML, LLMs, and RAGs are shared daily [1] Resource Sharing - The author encourages readers to reshare the content with their network if they find it insightful [1] Author Information - Avi Chawla (@_avichawla) shares the content [1]

If you found it insightful, reshare it with your network.Find me → @_avichawlaEvery day, I share tutorials and insights on DS, ML, LLMs, and RAGs.Avi Chawla (@_avichawla):Let's build an MCP server (100% locally): ...