AI Engineering and Architecture
Engineering notes on building AI systems whose behavior can be understood, evaluated, and improved systematically.
Notes by Valery Yuda. Email: valery.yuda@gmail.com. GitHub: valery-judah.
Content access
Some notes on this site may be intentionally obfuscated to discourage unauthorized automated scraping and use in LLM training. This is a temporary measure while I look for a more appropriate way to control machine access to and reuse of this content.
Selected Readings
A small selection of notes and drafts to start with.
AI Systems Require Behavioral Evidence
Why the quality of an AI system must be observed and measured under realistic conditions.
Evaluation
Drafts on evaluating AI systems, understanding their failures, and improving their behavior.
Natural Language Interface Substrate
A working model of how meaning, context, ambiguity, and intent shape natural-language interfaces.
Browse by Section
Use the site by major topic area when you want the broader map instead of a single highlighted note.
AI Engineering
Practical notes on LLM-based systems, evaluation workflows, and agent behavior.
System Design
Architecture, trade-offs, distributed systems, and interview-oriented design frameworks.
Engineering
General software engineering practices, architecture thinking, and implementation discipline.
Projects
Worked engineering concepts and design explorations grounded in practical system concerns.