Generative AI
Practical guide to integrating generative AI into systems — LLMs, RAG, agentic patterns, and the changing role of engineers.
2026
Model Context Protocol (MCP) Explained: How AI Agents Actually Connect to Your Systems
MCP went from an Anthropic open-source release in late 2024 to the industry standard for connecting LLMs to tools and data. Here is how the protocol actually works — hosts, clients, servers, tools, resources, prompts — why OpenAI and Google adopted it, and what to watch out for before exposing your systems to an agent.
Long Context vs RAG in 2026: Why 'RAG Is Dead' Keeps Being Wrong
With million-token context windows in Gemini, Claude, and GPT-4.1, 'just stuff everything into the prompt' sounds simpler than building a RAG pipeline. Here's why retrieval survives — cost, freshness, access control, and the physics of attention — and how long context actually changed RAG design instead of killing it.
Precision vs Recall: The Essential Difference That Shapes RAG Systems
Understanding precision and recall is critical for building effective RAG systems. Learn how these metrics determine retrieval quality, the precision-recall trade-off, and why a RAG system with poor recall misses critical information while poor precision drowns the LLM in noise.
AI Coding Assistance and Agentic Development: Why Engineers Must Evolve Beyond Writing Code
Tools like Claude, Cursor, and Codex are reshaping how software is built. The engineer's real value is shifting from writing code to setting boundaries, questioning AI output, and holding the wider context no assistant can match.