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 PracticeJul 4, 20268 min read

Questions to ask an AI development company

The right questions to ask an AI development company establish measurable safety, ownership, and cost guardrails before a single line of production code is written. You should leave the conversation with exact hallucination targets, a 90-day delivery cadence, and a clear post-launch SLA.

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AI Engineering PracticeJun 29, 20268 min read

Agent orchestration architecture: planner/executor tradeoffs

Agent orchestration architecture is the single design choice that determines whether your model-driven feature scales to 100s of requests per second or collapses under cost and latency. The planner/executor split reduces hallucination risk but raises token and invocation costs — and the wrong balance costs you $300k+ per year in hosting and engineering overrun.

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AI Engineering PracticeJun 25, 20267 min read

Production model selection: hosted APIs vs self-hosted models

Production model selection should start with your SLA, not the model paper. Choosing between hosted model APIs, self-hosted weights, or a hybrid arrangement is primarily an SLO, cost, and ops trade-off.

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AI Engineering PracticeJun 20, 20267 min read

RAG architecture: production tradeoffs and cost model

RAG architecture breaks when teams treat retrieval as an afterthought. Relying on a single hosted vector DB and ad-hoc caching delivers good demos but fails under 10k–100k monthly queries because cost, freshness, and latency interact in ways models alone can't mask.

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AI Engineering PracticeJun 14, 20267 min read

RAG evaluation framework: production metrics for retrieval

RAG evaluation framework: stop optimizing for model perplexity and start measuring retrieval’s business signals. A production-grade RAG evaluation framework ties recall@k, latency P95, and cost-per-query to end-user task success so engineering teams make decisions that actually move MRR and retention.

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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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