
How to Evaluate an AI Inbox Assistant Before You Trust It
A practical rubric for judging email AI on triage precision, false-archive rate, and draft acceptance — instead of vibes.

A practical rubric for judging email AI on triage precision, false-archive rate, and draft acceptance — instead of vibes.

Why keyword search fails in modern workspaces, and how semantic search, metadata, and permission-aware retrieval combine to make file search actually usable.

Chat is great for conversation, but grids give AI agents structured state, auditable steps, and natural human-in-the-loop checkpoints.

Skip the 'long context killed RAG' debate. Here's a practical decision framework based on cost, latency, recall, and freshness.

Chasing an empty inbox fails at scale. Priority-routing and AI-drafted triage workflows beat completion-based goals every time.

Task completion is a weak signal. Reliable agent evaluation needs trajectory analysis, tool-call correctness, and a real failure-mode taxonomy.

Benchmarks rarely predict production behavior. Here's how to choose an LLM by starting from task constraints — latency, cost, context, and tool use.

Embedded AI that categorizes files and drafts replies from your own data beats generic prompts. Here's how to build document workflows that actually save time.

A practical framework for testing GPT-4, Claude, and open models on spreadsheet formula generation — plus what the accuracy numbers actually mean.

Concrete prompting and verification techniques for coaxing correct, auditable formulas and clean data transforms from LLMs — and catching the silent errors.

A step-by-step walkthrough for creating specialized agents in Tamaton's agent framework, focused on document analysis and structured data extraction.

How multiple AI agents can edit the same document at once using Tamaton's conflict resolution, version control, and structured collaboration patterns.
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