Loading
Measuring AI Inference Latency Across CPU, GPU, and NPU Deployments
Measure AI inference latency across CPU, GPU, and NPU. Understand factors, benchmark methodology, and choose the right compute platform.
Architecture of Artificial Intelligence
Explore the AI architecture: input, processing, decision-making, output, and continuous learning. Key for modern AI like ChatGPT.
Benchmarking Hybrid RAG vs GraphRAG for Enterprise Knowledge Retrieval
Compare Hybrid RAG vs. GraphRAG for enterprise knowledge retrieval. Understand architectures, benchmarking, and trade-offs for optimal AI.
Implementing AI Request Tracing Across Microservices with OpenTelemetry
Implement AI request tracing in ASP.NET Core microservices with OpenTelemetry for end-to-end observability, diagnosing latency, and failures.
Building Versioned Knowledge Bases for Enterprise AI Systems
Build resilient enterprise AI with versioned knowledge bases. Ensure zero-downtime updates, instant rollbacks, and auditable RAG systems.
How do LLMs generate responses using tokens?
Discover how LLMs like GPT generate responses using tokens, from text conversion to next-token prediction and attention mechanisms.
Building AI-Powered Data Quality Validation Pipelines with Microsoft Fabric
Build AI-powered data quality validation pipelines in Microsoft Fabric. Ensure reliable AI & analytics with automated checks.
Securing MCP Servers Against Prompt Injection and Tool Poisoning
Secure MCP servers against prompt injection & tool poisoning. Learn AI-specific risks & implement practical production security measures.
Semantic Kernel Plugins vs Model Context Protocol: Choosing the Right Integration Strategy
Choosing between Semantic Kernel Plugins and MCP for AI integration? Learn their differences, strengths, and when to use each for robust enterprise AI applications.
Building Reliable AI Evaluation Pipelines Using Human and Automated Scoring
Build robust AI evaluation pipelines with human and automated scoring for reliable AI applications. Ensure quality and detect regressions.
End-to-End Prompt Versioning for Enterprise AI Systems
Master end-to-end prompt versioning for enterprise AI. Track, test, deploy, and monitor prompts like code for reliability and governance.
AI Personality Prompt Generator: Crafting Custom AI Personas with Ease
Effortlessly craft custom AI personas with the AI Personality Prompt Generator. Define tone, behavior, and boundaries for unique AI interactions.
What is MCP (Model Context Protocol)?
MCP (Model Context Protocol) standardizes AI interaction with external systems, tools, and data for enhanced functionality and real-world task execution.
AI-Generated Code Risk and the Software Supply Chain
AI-generated code introduces new software supply chain risks. Learn how to govern AI code for security, compliance, and trust.
Production-Ready AI Chat Applications in Blazor Using Semantic Kernel
Build production-ready Blazor AI chat apps with Semantic Kernel. Learn architecture, best practices, and deployment for scalable, secure LLM integration.
Implementing AI Memory in .NET Applications Using Semantic Kernel
Unlock AI memory in .NET with Semantic Kernel. Learn about types, stores, and best practices for context-aware applications.
Building RAG Applications in C# with Semantic Kernel and Azure AI Search
Build C# RAG apps with Semantic Kernel & Azure AI Search. Ground LLMs in your data for accurate, context-aware responses.
Part 5: Evaluating and Deploying AI
A practical guide to evaluating AI models, avoiding misleading metrics, handling overfitting and bias, improving explainability, and preparing AI systems for real-world deployment.
Part 4: The Neural Revolution And GenAI
A developer-friendly explanation of perceptrons, neural networks, deep learning, generative AI, large language models, next-token prediction, and diffusion models.
Part 2: Knowledge Representation and Reasoning in AI
A practical explanation of classical AI foundations, including search algorithms, knowledge graphs, ontologies, rule-based systems, and why explicit programming falls short.
AI Security: How AI assistants securely assess your personal data
Discover how AI assistants securely access your personal data using OAuth 2.0 OBO token delegation for safe, on-behalf-of interactions.
Part 1 : AI Foundation for Software Engineers: From Rules to Learning Systems
Explore AI for software engineers: from rule-based to data-driven learning systems. Understand AI's engineering discipline, not magic.
AI Agents vs Agentic Workflows: Understanding the Difference
AI Agents offer autonomy and dynamic planning, while Agentic Workflows provide predictability and control. Understand the difference for optimal system design.
Building AI-Powered Applications with OpenAI Responses API and .NET
Learn to build AI-powered .NET apps using OpenAI's Responses API. Integrate AI for chatbots, content generation, and more with practical examples.
Local LLMs with Ollama: Complete Setup and Integration Guide
Master local LLMs with Ollama! Setup, run, and integrate AI models like Llama and Mistral for privacy, cost savings, and offline access.
Building AI Applications with Gemini CLI and Gemini Code Assist
Learn how Gemini CLI and Gemini Code Assist help developers build AI-powered applications, generate code, automate workflows, and improve productivity.
Part3 - The Shift to Machine Learning
A practical explanation of how software engineering shifted from hand-written rules to machine learning systems that learn patterns from data.
What is Physical AI? Why the Next AI Revolution Will Move Beyond the Screen
Discover what Physical AI is and how it combines AI, robotics, computer vision, sensors, and Large Language Models to enable intelligent machines that perceive, reason, and act in the real world. Explore key technologies, real-world applications, challenges, and the future of AI-powered robotics beyond screens.
Model Context Protocol (MCP) Explained for .NET Developers
Understand Model Context Protocol (MCP) for .NET developers. Standardize AI integrations with tools, databases, and APIs for scalable, secure applications.
Building Production-Ready RAG Applications with Azure AI Search and .NET
Build production-ready RAG apps with Azure AI Search & .NET. Enhance LLMs with your data for accurate, context-aware responses.
