Field notes on production AI
Writing on agents, RAG, LLM engineering, and the architecture decisions that make systems hold up at scale.
Building Enterprise AI: A Guide to Governance and Compliance
Governance is not a checklist you fill in after launch. It is the operating system that keeps production RAG and agent systems safe, auditable, and cost-controlled at scale.
Building Enterprise AI: A Guide to Governance and Compliance
Governance is not a checklist you fill in after launch. It is the operating system that keeps production RAG and agent systems safe, auditable, and cost-controlled at scale.
Why Most AI Projects Break After the Prototype Stage
The demo works. The pilot ships. Six weeks later, the system is unreliable, expensive, and nobody trusts the output. Here is what actually goes wrong — and the maturity checklist that prevents it.
The Reference Architecture for LLM + RAG Systems That Survive Production
A layered pipeline that separates retrieval, reasoning, and validation — the reference architecture behind reliable LLM applications at enterprise scale.
Why LLM-Only Document Extraction Fails — And the Deterministic-First Fix
Connecting an LLM to a PDF parser works on the first ten documents and fails on the next ten thousand. A deterministic-first architecture for engineering document intelligence.
The Five RAG Pitfalls That Quietly Kill Enterprise Deployments
Chunking, retrieval, evaluation, access control, freshness — the five places RAG systems silently fail in production and how to fix each one.
Multi-Agent Systems That Hold Up Under Real Workloads
Why most agent demos collapse at scale, and the four architectural pillars — explicit state, deterministic orchestration, validation loops, cost-aware routing — that keep them reliable.
LLM Cost Engineering: 5–10x Cheaper Without Hurting Quality
Caching, routing, distillation, structured outputs — the disciplined practices that turn a runaway OpenAI bill into a controlled, observable line item.
Tutorial: Building Validation Loops in LangGraph
A code-first walkthrough of self-correcting agents using critique nodes, retry budgets, and structured outputs — the architecture pattern behind reliable extraction.
Case Study: Cutting Manual Document Processing by 80% for a Logistics Operator
A mid-market logistics operator was drowning in PDF bills of lading. A layout-aware OCR + LLM extraction pipeline replaced manual entry and unlocked operational data trapped in scans.