@DeRonin_: As an AI engineer in 2026, learn this: > systematic output reading. pattern recognition across 1,000 model responses is…

As an AI engineer in 2026, learn this: > systematic output reading. pattern recognition across 1,000 model responses is the actual skill, not writing the 1,001st prompt > context engineering. retrieval design, tool description quality, context window management. 90% of output quality lives here, not in the prompt > tool description discipline. naming, params, when-to-call guidance. write them like documentation, not afterthoughts > eval design as taste-building. golden sets, embarrassment-resistant scoring, LLM-as-judge calibration. evals aren't tests, they're how you develop AI judgment > model routing logic. task-type classification, tiered selection, fallback chains. defaulting to one frontier model is the most expensive junior tell > prompt versioning. git-style tracking, A/B testing in production, rollback strategy. every prompt is code, version it as code > confidence scoring as a structured output. self-assessment, critic models, threshold routing. low confidence should auto-route to human review > streaming response architecture. SSE patterns, partial UI rendering, perceived-latency optimization. loading spinners in 2026 are a UX tax > fallback chain design. primary → backup → cheap → cached. no production AI feature ships without this > latency budgets across user journeys, not per API call. one slow agent loop kills the flow even if individual calls are fast > failure mode cataloguing. taxonomy of how your agents, prompts, and retrieval break. that notebook becomes your real moat by month 6 > agent-vs-workflow-vs-single-call decision framework. knowing when NOT to use an agent is more valuable than any agent framework > failure post-mortems as portfolio content. engineers who get hired document real bugs they wrestled with, not demos they copied the article covers the curriculum. these decide if you're actually good at this