Practical guides, architecture notes, and lessons from shipping GenAI to production.
A production guide to preserving evidence lineage in RAG through chunking, normalization, summarization, reranking, and multi-hop synthesis with concrete data models, audit trails, and evals.
A production guide to safe retries in GenAI systems using workflow state, checkpoints, replay boundaries, deduplication, approvals, streaming, and observability.
A production guide to semantic diff testing for prompt, retrieval, reranker, and context changes—using pairwise adjudication, trace replay, risk scoring, and release gates to catch meaning-changing regressions.
A production guide to sampling traces, prompts, retrieval payloads, tool spans, and outputs in GenAI systems without losing debuggability, eval signal, or privacy.
A production guide to tuning retrieval and reranker thresholds per tenant, corpus, and query class in enterprise RAG systems, with evals, ACL-aware calibration, observability, and rollback controls.
A production-focused guide to making LLM agent tool selection consistent as prompts, schemas, models, and tool catalogs evolve.
A production-focused guide to isolating and sanitizing untrusted documents before enterprise RAG indexing, with risk scoring, sandboxing, staged release gates, provenance, and observability.
A production guide to GenAI observability that keeps logs useful while protecting sensitive data through redaction, tokenization, sampling, trace design, and privacy-aware evaluation.
A production-focused guide to enforcing JSON schemas and grammars at generation time, with architecture patterns, evals, latency tradeoffs, failure modes, and fallback strategies for contract-critical LLM workflows.
A production guide to GenAI load shedding under token, queue, and dependency pressure, with degradation ladders, admission control, evals, observability, and rollback criteria.
A production guide to designing GenAI decision logs so teams can reconstruct why systems answered, abstained, routed, called tools, or triggered guardrails.
A production guide to making hidden prompt state observable across system prompts, middleware, tool instructions, and framework scaffolding to prevent silent LLM behavior drift.