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.

AI product documentation has a unique challenge: you're documenting a non-deterministic system that will sometimes be wrong — how you communicate this determines whether users use AI features safely or dangerously.

LLM traces contain prompts, user data, and high span volume — collector-level tail sampling, PII redaction processors, and multi-backend routing control cost and compliance risk before data ever reaches storage.

Explainability in AI products is less about interpretable ML and more about building features that help users understand, verify, and trust AI outputs — and know when not to.

Eval scores measure model quality; business metrics measure product quality — for AI features, you need both, and they don't always move together.

Rolling out AI features safely requires more than standard canary deploys — the non-determinism of AI outputs means you need shadow mode testing, quality monitoring in production, and rollback that doesn't break user experience.