Last updated: May 10, 2026
Founder-led writing from QRUV Corp on the practical work behind AI systems: retrieval, evaluation, observability, cost controls, handoff, and the engineering harness that turns prototypes into useful software.
AI coding agents may write the code, but humans still have to set up the environment: permissions, tools, tests, sandboxes, guardrails, and observability.
Why the useful part of an AI project is often the APIs, workflows, evals, logs, permissions, and fallback behavior around the model.
A practical look at retrieval failure modes: chunking, permissions, stale data, missing evals, citations, and user trust.
A field guide for building LLM evals that help teams make release decisions instead of collecting decorative scores.
How small teams can control LLM spend with routing, caching, prompt budgets, batch workflows, and observability.
An opinionated checklist for AI consulting work that leaves behind maintainable systems, not just demos and slide decks.