GenerativeAI
How It Works
Knowlege Management, Graphs, and Retrieval Augmented Generation
Knowledge Management, Graphs, and Retrieval Augmented Generation
There is an odd sentiment in some quarters that GenAI renders knowledge management obsolete. In certain respects, GenAI can make KM less labor intensive. But, overall, GenAI only makes good KM more acutely valuable—i.e., the drag on productivity from poor KM practices becomes more severe and more glaring.
Of particular importance in legal, many applications* currently being built rely on KM methods like knowledge graphs and retrieval augmented generation (or “RAG”) in order to be performant and fit for purpose.
Recommended Reading
This article is part 1 of the series AI integration strategy for learning and knowledge management s
This article is part 2 of the series AI integration strategy for learning and knowledge management s
This article is part 4 of the series AI integration strategy for learning and knowledge management s
This article is part 3 of the series AI integration strategy for learning and knowledge management s
The Museu Picasso in Barcelona houses early works by Pablo Picasso, showcasing his classical painting skills before he developed his distinctive style. These paintings, completed when he was just fifteen, highlight his technical prowess as a traditional painter, including notable pieces like "Science and Charity" and "First Communion." Picasso's transition from these early works to his avant-garde style underscores the importance of mastering fundamentals before breaking new ground—a principle applicable to machine learning. In the realm of machine learning, understanding foundational concepts, such as embeddings, is vital before exploring advanced developments. Without this knowledge, machine learning models can remain mysterious black boxes, hindering progress. The document "What are Embeddings" aims to demystify these concepts, making them accessible to a generalist audience, including engineers, product managers, students, and anyone interested in mastering machine learning basics. It encourages building upon strong foundational knowledge to create innovative solutions, akin to Picasso's artistic journey.
The ever-increasing amount of unstructured data requires a paradigm shift and a new category of database management system - the vector database.
Vector databases have the capabilities of a traditional database that are absent in standalone vector indexes and the specialization of dealing with vector embeddings, which traditional scalar-based databases lack.
Vector databases have gained a resurgence in the AI community, and this is how they work.
Vector databases store data such as text, video or images that are converted into vector embeddings for AI models to access them quickly.
Vectors are on the rise throughout search, data & the entire fabric of the web, if not perhaps the X-factor, many enterprise apps now need to think about the V-factor.