Practical guides, architecture notes, and lessons from shipping GenAI to production.
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.
A production guide to memory architecture for LLM apps: prompts, session state, summaries, profiles, external stores, evals, privacy boundaries, and debugging stale or incorrect memory.
A production guide to LLM-as-judge evaluation programs that avoid bias, leakage, and false confidence through calibration, cross-model validation, human review, and drift monitoring.
A production guide to deterministic RAG preprocessing: stable parsing, canonicalization, chunking, metadata, hashing, lineage, regression tests, and rollout patterns that prevent silent retrieval regressions.
A production guide to enterprise RAG citation pipelines: passage-level attribution, stable IDs, span alignment, support scoring, validation, UX tradeoffs, and evals for trustworthy source links.
A practical guide to routing GenAI requests before orchestration: when to use lightweight prompts, full RAG, multi-step agents, or fast refusal based on risk, value, cost, and confidence.
A production-focused guide to making GenAI systems resilient when retrieval, rerankers, tools, memory, or validators fail partially—with fallback design, timeout budgets, retries, and observability.
A production-focused guide to evolving prompts, tools, retrieval payloads, and structured outputs in live LLM systems without breaking downstream consumers.
A practical guide to assembling, ranking, compressing, and ordering context in production GenAI systems to reduce hallucinations, latency, and cost without fine-tuning.