Practical guides, architecture notes, and lessons from shipping GenAI to production.
A production guide to reliable LLM agent tool loops: idempotency keys, retries, side-effect control, workflow state, duplicate suppression, compensating actions, and observability.
A production guide to confidence and abstention policies for GenAI systems: uncertainty signals, threshold tuning, evals, UX patterns, and observability to reduce false confidence.
A production-focused guide to reliable schema-bound LLM outputs: contract design, validation, repair, retries, evals, observability, and downstream safety.
A practical guide to prompt and response caching for LLM apps: semantic keys, invalidation, tenant isolation, freshness checks, safety guardrails, observability, and cost/latency tradeoffs.
A production guide to versioning the RAG knowledge layer with dual indexes, shadow rebuilds, retrieval diffing, rollback plans, and release gates to prevent silent quality or compliance regressions.
A production-focused guide to when GenAI systems should stop, ask, defer, or hand off to humans, with escalation policies, queue design, SLAs, auditability, and learning loops.
A production guide to monitoring and responding to drift in RAG systems: corpus changes, query shifts, retrieval regressions, answer-grounding decay, canary evals, alerting, and rollback.
A practical guide to tracing retrieval, prompts, tool calls, routing, and failures in production LLM systems so teams can debug regressions, connect quality to cost/latency, and improve faster.
A production-focused guide to building routing layers across fast, cheap, and premium models using cascades, escalation, specialization, evals, and fallback patterns that cut cost and latency without sacrificing quality or safety.
A production-focused guide to building ACL-aware enterprise RAG systems that prevent permission leakage from ingestion through retrieval, caching, reranking, and generation.
A production-focused guide to deciding when fine-tuning beats prompt, retrieval, or tool improvements—plus eval design, cost/latency tradeoffs, and operational guidance.
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