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.

AI outputs are probabilistic and sometimes wrong — the UX patterns that handle uncertainty, communicate limitations, and give users control determine whether users trust and adopt an AI feature.

Standard SRE SLOs measure availability and latency — an AI platform needs a third dimension, a quality floor, and an error budget policy that accounts for the fact that 'working' and 'correct' are different properties.

Streaming turns a 10-second wait into a live typing experience — here's how to implement streaming correctly end-to-end, handle errors in flight, and design the UX for a model that might change its mind mid-sentence.

Traditional product requirements assume deterministic software — AI features require a different spec format that handles variability, failure modes, and quality thresholds that don't fit in user stories.