Agentic AI, LLM engineering, and the models behind modern automation — multi-agent systems, LLM evaluation and comparisons, RAG, fine-tuning, AI infrastructure, security, and production AI engineering.
From the blog
Build an AI agent that generates structured clinical notes from encounter transcriptions, using SOAP format, template filling, and physician review workflows to improve documentation quality.
Learn how to build an AI agent that transforms natural language requirements into working, tested code. Covers prompt decomposition, language selection, code validation, and automatic test generation.
Design systems where multiple users interact with shared AI agents simultaneously, covering session sharing, conflict resolution, turn management, and maintaining consistent state across concurrent participants.
Build an AI agent that helps community members discover local events, register with capacity management, receive reminders, and get real-time updates for community organizations.
Build an AI agent that helps community members find local services for housing, food assistance, healthcare, and other needs with eligibility filtering, referral tracking, and follow-up support.
Build an AI agent that answers employee compensation questions including pay stub breakdowns, tax withholding explanations, benefits enrollment details, and HSA/FSA management — with strict data security.
Design an AI agent that continuously monitors competitors through web scraping, news aggregation, and LLM-powered analysis to generate actionable competitive intelligence alerts.
Build an AI agent that validates organizational policies against regulatory frameworks, identifies compliance gaps, and generates actionable remediation reports.
Learn how to build tool registries, tool factories, and shared tool modules that work across multiple agents. Covers composable design patterns, parameterized tools, dependency injection, and packaging tools for reuse.
