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.
Design a production-grade AI marketing copy generation API: architecture, prompts, guardrails, evaluation, and code examples.
A practical blueprint for building scalable, safe AI support chatbots—from NLU and RAG to orchestration, guardrails, and observability.
Practical strategies to optimize LLM context windows—reduce cost and latency while preserving accuracy with RAG, chunking, compression, caching, and evaluation.
