AI Engineering Practice

Production-grade RAG, agentic systems, evals, fine-tuning vs. prompting, model selection, observability, and AI ops — for technical leaders who want production AI, not demos.

AI Engineering PracticeJun 11, 20267 min read

Fine-tuning vs prompting: when to train models

Fine-tuning vs prompting is the single architecture choice that changes model cost, latency, and governance boundaries more than any other. Choosing wrong turns an engineering win into a recurring $100k+ bill or a compliance incident; choosing right saves latency, reduces inference spend by 30–70%, and makes behavior auditable.

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AI Engineering PracticeJun 7, 20267 min read

Model serving architecture: cost, latency, and routing tradeoffs

Model serving architecture is where product economics and SLOs collide — putting the latest large models behind a simple HTTP endpoint is cheap for demos but bankrupting at scale. You should treat routing and placement as first-class design decisions, not implementation details.

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AI Engineering PracticeMay 28, 20267 min read

RAG caching strategy: cut model spend and latency

RAG caching strategy is the single most effective lever to reduce both LM spend and end-user latency once you exceed roughly 10k monthly queries. A 60–80% cache hit rate typically cuts model costs by 50–75% and can drop p95 latency from 1.2s to under 300ms—if you accept the operational complexity of cache invalidation and coherence.

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AI Engineering PracticeMay 22, 20266 min read

Model monitoring platform: build vs buy for production AI

Model monitoring platform is the single place where your inference pipeline either becomes reliable or quietly bankrupts your roadmap. The build vs buy choice pivots on three numbers: your event volume, a senior engineering team's loaded cost (~$200k/yr), and the vendor price band ($7k–$30k/mo). Choose deliberately.

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AI Engineering PracticeMay 15, 20267 min read

Model selection at runtime: cost-aware routing for production AI

Model selection at runtime is the single production decision that separates cheap prototypes from sustainable AI features. When you route requests across models by cost, latency, and confidence, you can cut inference spend by 40–70% while preserving quality SLOs — but only if you build per-request scoring, caching, and fallback logic.

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AI Engineering PracticeMay 11, 20266 min read

Agent orchestration: scalable patterns for production agents

Agent orchestration determines whether tool-using agents are a product liability or a scalable differentiator. Agent orchestration is the coordination layer that runs planners, executes tools, enforces safety, and controls cost — and the right pattern reduces latency and cost by orders of magnitude compared with naïve per-request orchestration.

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AI Engineering PracticeMay 8, 20266 min read

Production RAG architecture: vector store, evals, cost

Production RAG architecture is primarily an engineering trade-off about data topology and cost, not just model choice. Published May 8, 2026 — this piece shows why vector stores, chunking, and retrieval evals drive 60–80% of your RAG TCO and production risk.

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AI Engineering PracticeMay 2, 20267 min read

Model evaluation pipeline: metrics, costs, and rollout

Model evaluation pipeline is the gatekeeper for production AI: shipping a new model without a disciplined pipeline increases user-facing regressions by 2–5× more than picking the wrong base model. A clear evaluation pipeline reduces failure modes and operational cost while increasing trust and rollout velocity.

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