A practical breakdown of AI agents — how they plan, use tools, manage memory, and orchestrate multi-agent workflows to solve complex tasks autonomously.
A developer's guide to Azure AI Foundry — the model catalog, deployments, prompt engineering playground, agent framework, evaluation tools, and building production AI applications.
An introduction to the Model Context Protocol (MCP), how MCP servers work, and why they are a game-changer for AI-powered development workflows.
A developer's guide to Retrieval-Augmented Generation (RAG) — the architecture, chunking strategies, vector databases, and when to use RAG over fine-tuning.
A practical guide to running large language models locally on your own hardware — covering Ollama, LM Studio, hardware requirements, and when local beats cloud.
How AI gateways help LLM apps control cost, latency, routing, caching, and policy enforcement in production.
A practical framework for selecting the right large language model — covering use case mapping, cost vs capability tradeoffs, latency, context windows, and deployment constraints.