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LLM Foundations

A working mental model of LLMs for people who think in systems: tokens, context windows, model selection, non-determinism, and the production AI stack.

  1. 01·What LLMs Actually DoA backend engineer's mental model for LLMs: next-token prediction, weights vs context, stateless APIs, and hallucination as a property to engineer around.
  2. 02·Tokens, Context Windows, and Your New Resource LimitsTokens are the byte of LLM systems: the unit of billing, latency, and memory. Budget a context window and cost a feature out before you ship it.
  3. 03·Your First LLM API CallDemystify LLM APIs with raw curl calls, message roles, the parameters that matter, streaming, and key hygiene, for backend engineers.
  4. 04·How to Choose an LLM in 2026: A Backend Engineer's Decision FrameworkA backend engineer's map of the mid-2026 LLM landscape: model tiers, real costs per million tokens, and a decision framework that actually works.
  5. 05·LLM Latency, Cost, and Non-Determinism in Production: Where Backend Instincts BreakThree properties of LLM calls that break backend intuition: non-deterministic output, alien latency profiles, and token-based cost. What to do about each.
  6. 06·The Production LLM Stack: Every Component, Mapped to Backend InfrastructureA component-by-component tour of a production LLM system, each piece mapped to backend infrastructure you already run, and the roadmap for this track.