The architectures, models, and patterns powering successful AI apps in 2026 — RAG, agents, function calling, evals, and cost control.
AI apps that work in production share four traits: a real workflow they replace, evals from day one, fallback to cheaper models, and a UI that earns trust.
RAG done well: chunk by semantic boundary, hybrid BM25 + vector, rerank with a small cross-encoder, cite sources inline.
Agents: keep them narrow — 1–3 tools, max 5 steps, hard timeout. Multi-agent demos rarely survive production.
Cost control: route 80% of traffic to a small fast model; escalate to a frontier model only when confidence is low.
Evals: golden dataset + LLM-as-judge + human review weekly. No evals, no shipping.
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