Day 01: Why model choice is rarely the first production AI problem
Why production AI succeeds or fails in the system around the model, not in the model choice alone.
Series
AI Systems Basics
A beginner-friendly foundation for understanding production AI systems. This series explains why AI is more than a model, how next-token generation works, why tokens and context windows matter, and how prompts and creativity settings become product decisions.
Posts in this series
Day 02: Why LLMs feel intelligent but still generate one step at a time
The practical reason LLM behavior needs constraints, validation, and repeatable output design.
Day 03: Why tokenization quietly affects cost, limits, and reliability
How a low-level text detail quietly becomes a product constraint for cost, latency, and reliability.
Day 04: Why more context can make an AI system worse
Why a larger context window does not remove the need for context discipline.
Day 05: Why vague prompts become vague systems
Why prompts become operational instructions once real users depend on the system.
Day 06: Why creativity settings are product decisions
How sampling settings shape the user experience, not just the writing style.
