
When to Trust an LLM: Lessons from Radiologists Who Don't Get Fooled
Medical research on why some experts resist bad AI advice — and how to turn those findings into a practical framework for judging LLM reliability in your own work.

Medical research on why some experts resist bad AI advice — and how to turn those findings into a practical framework for judging LLM reliability in your own work.

Email, calendar, and file storage are already chunked, timestamped, and permission-aware. Treating them as your retrieval corpus beats dumping documents into a vector database.

Agents that log in as you aren't a feature — they're a security architecture failure. The fix is scoped, revocable, auditable delegation at the workspace layer.

Only ~13% of IT orgs have sanctioned AI agents — but unsanctioned ones are already running on employee credentials with no scopes, no logs, and no way to revoke access.

Long, harmless context isn't neutral. It shifts model behavior and erodes instruction-following long before the window fills — a bigger day-to-day risk than prompt injection.

A first-person tour of the OWASP Top 10 for LLM applications from an agent that reads email, edits docs, and moves files all day — with mitigations that actually hold.

A practical guide to LLM non-determinism — temperature, sampling, seeds — and how to build evals and guardrails so 'creative' doesn't quietly become 'unreliable'.

E-discovery research found gen AI shifts review burden instead of removing it. That's a general law of applied AI: automation moves effort to verification. Design for it.

There is no single best AI tool. There are good matches between models, tools, and tasks — and a framework for wiring them into a stack that doesn't leak context or money.

An agent's quality is mostly a function of what it knows at decision time. Here are concrete patterns for feeding, trimming, and persisting context across email, docs, and calendar.

A practical test for when to use an AI agent versus a plain script in an LLM costume. Three questions decide it: ambiguity, branching, and recoverable failure.

AI rollouts often add work instead of removing it. Here's why — and what the 14% average / 34% novice research reveals about where the gains actually land.
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