Practical guides, architecture notes, and lessons from shipping GenAI to production.
A production-focused guide to hardening enterprise RAG and agent systems against prompt injection across retrieval, tools, web, and memory using layered controls, evals, and observability.
A production guide to implementing GenAI guardrails as executable policy layers across inputs, retrieval, tools, outputs, approvals, and side effects—without wrecking quality, latency, or developer velocity.
A production guide to choosing between embedding upgrades, rerankers, or both in RAG, with migration patterns, evals, dual-index backfills, rollout guardrails, and observability.
A production guide to shadowing GenAI changes on real traffic to catch regressions in prompts, retrieval, routing, and agent policies before user cutover.
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.