A basic introduction to AI concepts

The three decisions that determine whether an AI project survives contact with production, build vs. buy vs. rent, architecting for real load, and handling failures that don't announce themselves.

The open standard for connecting AI models to real tools, data, and systems.

Grounding model output in real, retrieved data instead of letting it answer from imagination.

Shipping AI features into real products, stack choices, guardrails, and engineering practices once model behavior is part of the app.

Plain-English definitions for the AI terms that actually show up across these articles, tokens, embeddings, RAG, latency, context windows, and the rest, so the jargon never blocks the point being made.

Reusable architecture and coding patterns that reduce accidental complexity.

Core principles we use to keep delivery predictable and quality high.

Cloud deployment and platform patterns we apply for secure operations.

Operational patterns for distributed systems and managed cloud services.

Document-model data design for flexible product evolution.

Relational modeling and transaction strategy for business-critical data.

Patterns for robust services and domain modeling in .NET ecosystems.

Backend runtime practices for resilient APIs and integrations.

Component architecture and state models for maintainable user interfaces.

Language fundamentals and runtime behavior that affect reliability in production.

TBD