Practical guides, architecture notes, and lessons from shipping GenAI to production.
A production-focused guide to planning GenAI capacity: tokens, concurrency, queues, rate limits, burst behavior, backpressure, and launch-day resilience across RAG and agent systems.
A production guide to feature-flagging prompts, retrieval, model routes, tools, and guardrails so GenAI teams can ship, test, target, and roll back safely without full redeploys.
A production-focused guide to using MCP as a control plane for enterprise LLM tools, with patterns for contracts, auth, isolation, approvals, observability, evals, and rollout.
A production-focused guide to safely expanding LLM agent tool permissions with risk tiers, policy layers, shadow execution, approvals, tenant rollouts, audit trails, and evals.
A production guide to building and maintaining a high-signal RAG golden set: real queries, retrieval and answer labels, edge cases, drift refreshes, and release-gate evals.
A production-focused guide to setting freshness targets for enterprise RAG systems and designing ingestion, reindexing, cache invalidation, and retrieval around real business risk.
A production-focused guide to tenant-aware evals and release gates for multi-tenant GenAI systems, with leakage probes for RAG, caches, agents, memory, and observability.
A practical guide to adding reranking to production RAG: architecture, model choices, candidate sizing, score fusion, evals, latency/cost tradeoffs, and rollout patterns.
A production guide to safely canary prompts, retrieval, rerankers, models, and routing policies with traffic shaping, guardrails, evals, and rollback triggers.
A production guide to semantic caching for RAG: answer vs retrieval caching, ACL isolation, grounding revalidation, freshness invalidation, hit risk scoring, and observability to cut cost and latency safely.
A practical guide to treating prompt optimization as an engineering discipline: experiments, versioning, routing, evals, regression gates, and observability before fine-tuning.
A practitioner’s guide to query rewriting in production RAG: when to rewrite, expand, or decompose queries; how to preserve intent and permissions; and how to evaluate cost, latency, and failure modes.