A practical, data-driven guide comparing prompting vs. fine-tuning for LLM apps, with decision checklists, trade-offs, and implementation tips.
Practical blueprint for an AI content personalization API: architecture, endpoints, models, metrics, latency, and safety—built to scale.
Build a production-ready AI email assistant: architecture, Gmail/Graph integration, LLM prompts, security, reliability, and code examples.
A clear, visual walkthrough of Transformer architecture—from tokens and positions to multi-head attention, residuals, and FFNs.
A practical guide to multi‑turn conversational AI: architecture, memory, grounding, safety, and evaluation patterns for reliable, scalable assistants.
A practical, secure guide to setting up a Model Context Protocol (MCP) server in TypeScript and Python, wiring clients, and hardening for production.
