Context Engineering, Data Mesh Architecture Hold Keys To Addressing AI Data Shortage

Welcome to this Cloud Wars Agent and Copilot Minute. In these discussions, I’ll be analyzing opportunities, impact, and outcomes possible with AI; today’s focus is an impending shortage of novel data for training AI.

Highlights

00:32 — It’s important to design enterprise data estates to accommodate this shortage, focusing on understanding rather than just storage. Large models like GPT-4, Claude, and Gemini have already consumed most of the publicly available data, necessitating better and more contextual data for the next generation of models. The potential result: a stagnation in new insights.

01:34 — Businesses need to move from collecting everything to collecting the right things, emphasizing the importance of qualitative information. Contextualizing data — or context engineering — involves adding annotations and captions to images and other data points to provide models with the necessary context.

01:57 — Context engineering involves adding business meaning to raw data, encoding relationships, business rules, and semantics, and organizing information in ways that LLMs can reason over. Microsoft Fabric, with its Medallion Architecture, is a key tool for building context-engineered data estates. It consists of three tiers: bronze for raw data, silver for operational or clean data, and gold for analytical data.

02:39 — Data mesh involves creating data products designed for different business domains, such as HR and finance, to produce context-ready data states. Each domain in the business owns its own data product, not just a table or a dump of fields, but a finished, documented, governed, and contextualized asset. This approach is crucial for AI engineering, as it allows for the creation of centrally governed data assets that can be handed over to business users.

03:40 — Data platforms can act as context engineering houses, provisioning data sets that business users can decorate with the necessary information for AI models. The data mesh style architecture and a context engineering mindset enable businesses to build data estates that copilots, agents, and LLMs can reason over.

More AI Insights:

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• How To Choose the Right Microsoft Tools for Your AI Agent Orchestration Use Cases

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