Prompt engineering has evolved from simple question-and-answer interactions to sophisticated patterns that unlock Claude’s full potential in Azure AI Foundry. As enterprises deploy Claude at
Author: Chandan
Model Context Protocol Part 4: Enterprise Integration Patterns – Security, Scaling, and Production Deployment
Master enterprise MCP integration with OAuth 2.1 authentication, role-based access control, monitoring, scaling, and security best practices. Production-ready patterns for deploying MCP servers at scale.
Model Context Protocol Part 3: Building MCP Servers in Node.js and C# – Cross-Platform Implementation Guide
Master MCP server development across platforms. Comprehensive guide comparing Node.js TypeScript and C# .NET implementations with production-ready code examples, deployment strategies, and platform selection guidance.
Model Context Protocol Part 2: Building Your First MCP Server with Python and FastMCP
Learn to build production-ready MCP servers with Python and FastMCP. Step-by-step guide covering tools, resources, database integration, error handling, testing, and Claude Desktop integration with complete working examples.
Model Context Protocol Part 1: Understanding the New Standard for AI-Data Integration
Explore the Model Context Protocol (MCP), the emerging standard for AI-data integration. Learn MCP’s client-host-server architecture, JSON-RPC messaging, capability negotiation, and how it solves the enterprise AI integration challenge.
Vector Databases Part 8: Lessons Learned and Reality Check
The vector database market exploded from $2.2 billion in 2024 with promises of revolutionizing AI applications, yet the reality reveals a stark gap between marketing
How to Setup Docker: Complete Installation Guide for Linux, Windows, and macOS
Docker has transformed modern software development by enabling developers to package applications with all dependencies into standardized containers. Whether you are building microservices, setting up
Vector Databases Part 7: Production Deployment Patterns and Operations
Moving vector databases from development to production requires addressing challenges that prototype implementations ignore including high availability, disaster recovery, cost optimization, and operational monitoring. Production
Vector Databases Part 6: GraphRAG Architecture and Knowledge Graphs
Traditional RAG systems excel at finding semantically similar documents but fail catastrophically when queries require connecting information across multiple sources or understanding dataset-wide themes. A
Vector Databases Part 5: Advanced Optimization and Reranking Strategies
The gap between acceptable and exceptional RAG performance often comes down to optimization decisions made after basic implementation. Production systems require careful tuning of reranking
