Deep dives into Agentic Workflows, distributed systems, and the architectural rigor required to move AI from experimentation to enterprise-grade production.
In production AI, choosing whether to rerank every query or apply selective reranking is a decision about precision, latency, and cost budgets.
In production AI, retrieval quality hinges on both how we fetch information and how we present it. Reranking after retrieval refines a candidate set using learned signals, while query expansion broadens a query before retrieval, increasing recall but risking noise.
In production AI, governance is not a luxury feature—it is a core driver of speed, reliability, and accountability. The practical choice is not between ethics and speed, but between embedded guardrails that scale with product teams and a spreadsheet-heavy approach that slows shipping and invites risk.
In production AI, caches determine whether responses reach users within strict SLAs and whether results stay trustworthy as data evolves.
In production AI systems, evaluating retrieval versus generation touches every facet of deployment—data governance, evaluation workflows, latency budgets, and risk controls.
In production AI, protecting both the data that informs a model and the instructions that guide its responses is a dual responsibility.
Production AI systems demand resilience beyond best-case performance. When a chosen provider encounters latency or failure, the system should degrade gracefully without harming business outcomes.
In production AI programs, aligning models with business goals requires more than clever prompts or slick dashboards. RLHF and DPO are two principled paths to preference alignment, each with distinct data requirements, governance needs, and deployment tradeoffs.
In enterprise AI programs, the way you prompt models shapes not only outputs but governance, safety, and speed-to-value.