Production AI field notes
Production AI Engineering Field Notes
Practical, source-backed playbooks for designing, evaluating, securing, and operating AI agents, RAG, and ML systems.Start here
The blog is organized around the work I actually ship: retrieval quality, agent reliability, and production ML operations.RAG & evaluationRetrieval design, chunking, offline evals, and answer quality gates.
Agents & governanceEnterprise assistants, guardrails, escalation paths, and operating rules.
MLOps & observabilityDeployment, monitoring, cost, latency, traces, and incident response.
Featured articleApril 9, 20264 min readRAG EvaluationRAG
A layered RAG evaluation framework for datasets, retrieval, grounded answers, citations, operations, release decisions, and production feedback. More articles
April 13, 20264 min readAI EvaluationRegression Testing
A blueprint for detecting meaningful AI quality regressions with stable baselines, segmented signals, release checks, and alerts that lead to an actionable diagnosis.April 12, 20264 min readLLM CostCost Optimization
A production method for reducing LLM cost through measurement, caching, routing, context control, and workload design without hiding quality regressions.April 12, 20264 min readAI SecurityPrompt Injection
A defense-in-depth checklist for prompt injection, untrusted retrieval, tool permissions, argument validation, sensitive data, confirmations, and incident response.April 11, 20264 min readMultimodal RAGDocument AI
A production-oriented design guide for parsing, indexing, retrieving, citing, and evaluating text, images, and tables in a multimodal RAG system.April 11, 20264 min readSynthetic DataFine-Tuning
A controlled pipeline for generating, filtering, versioning, and evaluating synthetic domain data without hiding contamination, policy, or distribution risks.April 10, 20264 min readAgent EvaluationAI Agents
A practical evaluation loop for tool-using AI agents, covering task datasets, trace-level scoring, release decisions, production feedback, and failure ownership.April 10, 20265 min readInference EngineeringvLLM
A sourced vLLM serving blueprint covering workload design, continuous batching, KV-cache pressure, admission control, observability, capacity tests, and safe rollout.April 8, 20264 min readLLM ObservabilityObservability
A production observability model that connects LLM traces, retrieval and tool behavior, quality signals, cost, alerts, and incident response.April 7, 20264 min readAgent ArchitectureAI Agents
A practical decision framework for choosing chains, routers, planners, stateful graphs, and deterministic controls according to workflow risk.April 6, 20264 min readAI GovernanceResponsible AI
A delivery-oriented governance framework that connects AI risk tiers, technical controls, release evidence, decision rights, and production response.March 28, 20264 min readRAGRetrieval-Augmented Generation
A practical production RAG blueprint covering source governance, ingestion, hybrid retrieval, grounded answers, evaluation, and safe rollout.March 20, 20264 min readMLOpsMachine Learning
A release and operations checklist for ML systems covering data contracts, reproducibility, tests, registry, rollout, monitoring, ownership, and rollback.March 10, 20265 min readAI MetricsEnterprise AI
A practical measurement model linking AI quality to workflow outcomes, reliability, adoption, unit economics, risk, and explicit product decisions.
