Technical deep dives
Technical deep dives
Architecture, code, and trade-offs for engineers and technical leads. Each one pairs with a short, plain-English post.
Five GenAI Patterns and Their Technical Blueprints
Reference blueprints for the five projects that ship fastest: support, document Q&A, drafting, search, and reporting.
Read →
Designing Human-in-the-Loop Workflows for GenAI
Where to place review, how to route by risk and confidence, and how to turn corrections into improvements.
Read →
Measuring GenAI Quality: Offline Evals, Online Metrics, and A/B Tests
A measurement stack for GenAI: golden sets, model judges, production signals, and controlled rollouts.
Read →
Build vs Buy for GenAI: An Engineering Decision Framework
How to decide between off-the-shelf tools, configurable platforms, and custom builds, including the hidden costs of each.
Read →
Secure GenAI Architecture: Protecting Data End to End
Threats, controls, and patterns for GenAI systems: redaction, access control, prompt injection, and audit.
Read →
Scoring GenAI Use Cases With a Weighted Model
A transparent scoring method for choosing between candidate projects, with a small script you can reuse.
Read →
From Pilot to Production: A GenAI Readiness Checklist
The engineering gaps between a convincing demo and a system people rely on, with a checklist to close them.
Read →
A Total Cost of Ownership Model for GenAI Systems
A worked cost model: build, run, and operate, with the levers that move each line.
Read →
RAG vs Fine-Tuning: A Technical Decision Guide
When retrieval is enough, when fine-tuning pays off, and how to test the choice instead of arguing about it.
Read →
How LLMs Work for Practitioners: Tokens, Context, Sampling, and Cost
The mechanics that explain price, latency, and quirks, in the depth an engineering lead needs.
Read →
Our GenAI Delivery Framework: Eval-Driven Development in 30 Days
How we structure a month-long build around an evaluation set, so quality is measured from week one.
Read →
Production Document Q&A: Chunking, Hybrid Search, Citations, and Evals
The engineering choices that decide whether a document assistant is trusted: how you split, search, cite, and test.
Read →
Designing Reliable AI Agents: Tool Use, Control Loops, and Failure Modes
An agent is a loop with tools and limits. Here is how to build one that behaves predictably in production.
Read →
Engineering a Support Copilot: Retrieval, Drafting, and Review
A technical walk through ticket intake, hybrid retrieval, grounded drafting, confidence gating, and the feedback loop.
Read →
A GenAI Reference Architecture and Delivery Roadmap
The platform pieces you need before the second GenAI project, and the order to build them in.
Read →