Decoding AI

Decoding AI teaches engineers to design, build and deploy AI software through Decoding AI Magazine, books and practical courses. Its weekly guides cover data collection, system design, deployment, monitoring and evaluation. The LLM Engineer’s Handbook presents an end-to-end framework for LLM and retrieval-augmented generation applications, while the free LLM Twin course lets learners build an AI character that writes in their style. Its Agent Engineering course, made with Towards AI, serves software and data professionals moving into AI engineering.

Founder and operator Paul Iusztin is the handbook’s author and Agent Engineering’s lead instructor. He renamed Decoding ML to Decoding AI Magazine in 2025, reflecting broader coverage of LLMs, RAG, agents and LLMOps. Its educational implementations emphasize the infrastructure around models: LLM Twin separates data collection, feature processing, training and inference into four Python microservices. Building a Coding Agent From Scratch uses Decode to teach permissions, sandboxing, memory and evaluation around a shared core supporting interactive terminal use and remote execution.

As of August 2026, Decoding AI Magazine advertised 40,000+ subscribers, described as engineers. Its LLM Twin repository had approximately 4,400 GitHub stars and 731 forks. Sponsorship from Modal, Opik and Kitaru supports the free coding-agent course.