Essays
Consulting essays that work through the operating problems of AI in the enterprise, with the rigor of a working paper and the directness of an operating note.
When to Fine-Tune vs. Prompt vs.Tools
A practical decision framework to choose between prompting, tool use (RAG/APIs), and fine-tuning—based on stability of knowledge, control needs, latency, data availability, and cost.
Versioning Prompts, Policies, and ModelsTogether
Ship sets, not parts. A practical release pattern to version prompts, policies, routing, evals, and models together—so you can roll forward safely (and roll back fast).
The “Two-Model” Pattern for Cost &Reliability
Cheap first, smart second—route only when needed. A practical routing pattern that cuts spend, protects latency budgets, and lifts reliability.
Synthetic Data: Where It Helps (and Where It Hurts)
Understanding when synthetic data accelerates AI development and when it risks misleading results, with practical patterns and guardrails.
Structured Retrieval with SmallAdapters
Marry structured stores with vector recall using lightweight adapters—gain precision without a ground-up rebuild.
Stop Debating—Start Measuring: Practical LLM EvalLoops
Golden sets, rubric scoring, and error taxonomies that travel across teams. A practical, repeatable loop to evaluate LLM quality and ship with confidence.
Retrieval-Augmented Generation: Design Patterns forScale
A practical catalogue of RAG patterns—chunking, hybrid retrieval, reranking, provenance, freshness, and cost/latency controls—to scale reliable retrieval-augmented systems.
Retrieval Latency: Where the MillisecondsHide
Pinpoint and reduce latency across the retrieval stack — from query parsing to embedding lookup to vector store fetch — to scale AI applications without performance trade-offs.
Red Team Notes: Jailbreaks We ActuallySee
Real jailbreak patterns we see in production—and the mitigations that actually help: injection hardening, instruction isolation, tool gating, and oversight loops.
RAG Isn’t a Silver Bullet—But This Setup WorksOften
A pragmatic Retrieval-Augmented Generation setup: when to use RAG, how to chunk and ground, and when to skip it entirely for better reliability and latency.
Prompt Surfaces: Where Do Prompts Live?
Inline, panels, slash-commands, and background agents—when and where to place prompts so people move faster with less error.
PII/PHI: A Practical SegmentationPlaybook
Tokenization, masking, and role-aware access zones that actually ship. A pragmatic playbook to segment PII/PHI so teams can build safely without stalling delivery.
Observability: What Matters BeyondTokens
Answerability, latency budgets, and drift—not just spend. A practical observability blueprint for production AI systems.
Min-Posture Pipelines: Good Enough toShip
Ship useful data pipelines fast with late-binding semantics, idempotent loads, and rollback levers—without waiting for a perfect platform.
Micro-telemetry: What to Log forLearning
Edits, reverts, abandonments, overrides, and dwell-time—the micro-signals that actually improve AI assistants. A minimal event model, derived metrics, and privacy-first instrumentation.
MLOps, Observability & Cost/Performance
Consulting essays on two-model routing, observability beyond tokens, batch vs. streaming, actionable cost postmortems, and versioning prompts/policies/models together.
Incident Review Template for AIFailures
A practical, blameless incident review template for AI failures—capture context, classify errors, assign fixes, and close the loop with measurable controls.
Guardrails as Product, NotAfterthought
Treat safety as a first-class product capability—owned, measured, and iterated. How to build guardrails with roadmaps, telemetry, and user experience that accelerates delivery.
From Demos to DailyUse
Transform AI from showcase demos into daily-use tools by fixing friction, optimizing workflows, and embedding trust-building patterns before chasing delight.
Foundation Models &Retrieval
Consulting essays on RAG patterns, when to fine-tune vs. prompt vs. tools, embedding drift, retrieval latency, and structured retrieval with small adapters.
Explainability that Practitioners Can LiveWith
Transparent rationales, uncertainty, thresholds, and quick overrides—explainability clinicians, operators, and analysts can actually use without blocking action.
Why Pilots Stall and What to Do About It
AI pilots stall for predictable reasons. No platform. No funding cadence. No decision rights. The four patterns that determine which pilots scale.
Evaluation, Safety &Guardrails
Consulting essays on practical LLM evaluation loops, real jailbreak red-teaming, practitioner-grade explainability, building guardrails as product, and an incident review template for AI failures.
Evaluation Sets from Real WorkArtifacts
Mining tickets, emails, and documents to build evaluation sets that actually reflect production use—without leaking sensitive data or skewing results.
Error States that BuildTrust
Design error states that build trust: graceful fallbacks, show-your-work evidence, recovery paths, and policy-aware messaging—without breaking flow.
Embedding Drift: Detecting When “Meaning”Moves
A lightweight approach to detect semantic drift in embeddings using canary queries, centroid distance, and anchor pairs—before quality and risk degrade.
Data Debt: The Quiet Tax on Every AIIdea
Why unaddressed data debt silently inflates AI costs and timelines, and the concrete steps to reduce it before model work begins.
Cost Postmortems That Actually ChangeThings
From “too expensive” to specific routing, caching, and prompt changes. A practical template for AI cost postmortems that reduce spend without tanking quality.
Batch vs. Streaming for AIWorkloads
When nightly jobs beat real-time (and vice versa). A practical guide to choosing batch or streaming for AI pipelines based on latency, cost, and risk.
Human-in-the-Loop UX
Consulting essays on human-in-the-loop UX: AI confirm/override, prompt surfaces, micro-telemetry, trust-building error states, and moving from demos to daily use.
AI that Asks Before ItActs
Designing confirm/override steps that speed up rather than slow down AI-assisted work.
Cost Economics of LLMs: The Real Unit Cost of anAnswer
Token pricing is the headline. The unit economics live in retries, retrieval, caching, and override. A framework for measuring what an answer really costs.
Agent Orchestration: When One Model, When aCrew
Multi-agent systems are seductive and often unnecessary. The pragmatic rules for choosing between a single model with tools and an orchestrated crew.