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 for pool service companies that optimizes service routes, calculates chemical dosages, diagnoses equipment issues, and manages seasonal opening and closing schedules.
Build an AI agent that delivers personalized post-operative care instructions, monitors patient recovery through symptom check-ins, triggers clinical alerts when needed, and schedules follow-up appointments automatically.
Learn how to build an AI agent that monitors competitor prices, evaluates price match requests against policy rules, calculates adjustments, and communicates price matches to customers — protecting margins while staying competitive.
Implement an AI pricing agent that applies dynamic pricing rules, generates professional quotes, performs competitor price analysis, and handles discount approval workflows.
Design and deploy a fully private AI agent using self-hosted LLMs. Cover infrastructure requirements, model selection, security best practices, and cost comparison with cloud APIs.
Learn how to build an AI agent that helps shoppers discover products through natural language search, personalized filtering, side-by-side comparisons, and recommendation engines tailored to individual preferences.
Design and implement an AI recommendation agent that combines user preference modeling, inventory-aware filtering, and LLM-powered explanation generation for personalized product suggestions.
Design and implement a centralized prompt registry with API access, tagging, search, and role-based access control. Learn how teams can share, discover, and manage prompts at scale.
Learn how to build an AI agent that answers buyer questions about property listings around the clock, including database lookups, FAQ handling, photo sharing, and automated showing scheduling.
