Practical guides, architecture notes, and lessons from shipping GenAI to production.
A production guide to GenAI SLOs beyond uptime: quality, grounding, latency, abstention, tool success, cost, safety, and how to turn them into budgets, alerts, release gates, and runbooks.
A production guide to speculative retrieval in RAG: intent prediction, partial-query prefetch, cancellation, ACL safety, observability, evals, and cost/latency tradeoffs.
A production-focused guide to streaming structured JSON from LLMs safely despite truncation, retries, schema drift, and disconnects, with architecture, evals, and fallback patterns.
A production-focused guide to counterfactual evaluation for GenAI systems: isolating the causal impact of retrieval, routing, prompts, tool policy, and guardrails with replay, interventions, and attribution.
A production-focused framework for deciding when long-context models beat RAG, when retrieval wins, and how to evaluate hybrid architectures under real cost, latency, and recall constraints.
A production-focused guide to testing whether your corpus can answer expected RAG queries, uncovering corpus gaps, metadata blind spots, and unanswerable classes before launch.
A production-focused guide to moving long-running LAG and tool-using LLM tasks out of the request path with async APIs, queues, resumable workflows, retries, observability, and SLA-aware execution.
A production guide to designing agentic and RAG systems around token limits, with budgeting, compression, adaptive context, routing, evals, and observability patterns.
A production guide to circuit breakers for LLM agents: stop runaway tool loops with runtime trip conditions, budgets, side-effect containment, fallbacks, observability, and evals.
A production-focused guide to building offline replay harnesses for GenAI systems with real traces to validate prompt, retrieval, routing, and tool changes before release.
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.