Field notes · page 2 of 3
AI Engineering Articles, Page 2
From the review desk
Production AI systems: inference and serving, retrieval, agents, evaluation and AI security, multimodal and edge pipelines, and AI-native product architecture.
Articles, page 2
Build an LLM Evaluation Harness That Can Block a Release
From task contracts and test slices to calibrated judges, regression budgets, and production feedback loops.
Hallucination Controls Belong in the System, Not One Prompt
A layered design for constraining claims, grounding evidence, verifying outputs, and abstaining when the system does not know.
Guardrails as Policy Enforcement, Not Keyword Blocking
Building layered input, action, and output controls that remain testable when language and threats change.
Threat Modeling an AI Application End to End
Assets, trust boundaries, model-specific attacks, tool abuse, data leakage, and concrete mitigations for deployed AI systems.
Prompt Injection Defense in Depth
Why delimiters are not a sandbox, how indirect injection reaches agents, and which architectural controls actually reduce impact.
Embedding Systems: Models, Indexes, and Semantic Drift
Selecting representations, constructing embedding inputs, migrating indexes, and detecting when semantic neighborhoods stop serving the product.
Fine-Tuning with LoRA in Production
When fine-tuning is justified, how low-rank adapters work, and what it takes to evaluate, serve, and update them safely.
Multimodal AI Pipelines for Images, Audio, and Documents
Engineering preprocessing, temporal and spatial grounding, context budgets, validation, and storage around multimodal models.
LLM Cost Engineering: From Token Prices to Unit Economics
Modeling full request cost, eliminating waste, forecasting margins, and optimizing without disguising quality regressions.
Observability for LLM and Agent Systems
Tracing model calls, retrieval, tools, state transitions, quality signals, and cost without turning telemetry into a privacy liability.
Model Routing, Cascades, and Fallbacks
How to choose models per request using task risk, calibrated confidence, operational health, and total expected cost.
Structured Outputs Beyond Valid JSON
Designing schemas, constrained decoding, semantic validators, repairs, and safe evolution for dependable model integrations.
In the age of AI
The advantage was never the model. It's knowing what to build with it — and having a team that can actually ship it.
That's the part I help with: finding where AI genuinely makes your business faster, deciding what's worth building, and standing behind it once it's live.
Four offices, one very full passport
Every dot on this map is a conversation I still remember.
- Where I've spoken
- Office
