THE SKILL GAP THAT WILL SEPARATE AI WINNERS
A recent talk argues the real edge isn’t flashy models but the ability to turn ad‑hoc prompting into repeatable, measurable workflows. The focus is on problem f...
A recent talk argues the real edge isn’t flashy models but the ability to turn ad‑hoc prompting into repeatable, measurable workflows. The focus is on problem framing, packaging the right context, and running tight feedback/evaluation loops so AI output can safely ship to production.
Teams that operationalize AI with evals and observability will ship faster with fewer regressions.
Process skills (framing, context, evals) often matter more than picking the 'best' model.
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Build a small, versioned eval set for one recurring backend/data task (e.g., SQL generation or schema mapping) and compare LLM vs baseline on accuracy, latency, and cost.
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Put prompt+context templates under source control and run them in CI with diff checks and automatic rollback on degraded eval scores.
Legacy codebase integration strategies...
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Add an AI assist step to existing pipelines behind a feature flag and log outcomes to grow an eval set before enabling by default.
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Introduce context packaging (schemas, sample rows, contracts) without changing core jobs and monitor drift and error modes.
Fresh architecture paradigms...
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Design AI interaction patterns up front (prompt templates, eval harness, observability, cost budgets) as first‑class modules.
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Pick APIs and SDKs that expose tokens, latency, and model versions to enable governance and automated gating from day one.