The databases underpinning AI applications have evolved beyond adding a vector column to Postgres — here's the full picture of AI-native data patterns and when to use each one.
Picking the right model is an engineering decision with cost, latency, quality, and reliability tradeoffs — here's a framework for making that decision systematically rather than defaulting to the most capable model for everything.
LLMs produce prose by default — getting reliable, schema-valid structured output requires constrained decoding, output parsing strategies, and validation layers that catch the cases where the model still gets it wrong.
Arize Phoenix, Traceloop's OpenLLMetry, and Langfuse all build on OpenTelemetry but make different tradeoffs on what's pre-built versus what you configure yourself — the comparison that matters before picking one.
Embedding models and vector databases are the backbone of RAG systems — here's how to choose embedding models, design vector indexes, and operate vector search reliably at enterprise scale.
Reasoning models spend tokens thinking before answering and dramatically outperform chat models on hard problems — but they cost 10-20x more and are slower, so the choice requires understanding which tasks actually need deep reasoning.
Product teams build shadow AI infrastructure when the platform team's process is slower than doing it themselves — a self-service catalog of golden-path templates removes that incentive without removing governance.
Multimodal AI handles documents, images, and audio natively in 2026 — here's how to build production systems that process non-text inputs reliably, cost-effectively, and at scale.
Prompt caching lets providers reuse computed key-value states for repeated context — reducing costs by 70-90% and latency by 80% for applications with stable system prompts or large documents.
Fine-tuning has become accessible and cheap, but prompt engineering still outperforms it for most production use cases — here's the honest decision framework for choosing between them.