Custom copilots and the seven pillars of modern AI development
Navigate Modern AI: Your Guide to Custom Copilots and the Seven PillarsBusinesses have many new opportunities to manage, retrieve, and use knowledge in an era of rapid technological advancement and exponential information consumption
Copilot pattern-based solutions are exciting, but businesses must carefully consider the design elements to create a durable, adaptable, and effective approach How can AI developers ensure their solutions attract attention and engage customers?
These connectors link data silos, making valuable information searchable and actionable. Microsoft Fabric lets developers ground models on enterprise data and seamlessly integrate structured, unstructured, and real-time data
Metadata and role-based authentication enrichEnriching raw data improves, refines, and values it. Adding context, refining data for AI interactions, and data integrity are common LLM enrichment goals
LLM features often use proprietary data. A smooth and effective model requires simplifying data ingestion from multiple sources. Adding templating can make enrichment more dynamic
Azure AI Search with vector search leads this transformation. Azure AI Search with semantic reranking provides contextually relevant results regardless of search keywords
Copilots let search processes use internal and external resources to learn new things without model training. By constantly incorporating new knowledge, responses are accurate and contextual, giving search solutions a competitive edge