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

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.

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

How LLM context window limitations create compounding inefficiencies across email, documents, and calendars — and what to do about it.

Architectural patterns that give agents persistent memory across emails, documents, and projects — without exponential token costs.

Context switching isn't just a human problem. For AI agents, it's a measurable performance tax that only a unified data layer can eliminate.

A technical framework for preserving state and context when multiple AI agents collaborate on complex workflows.

A practical guide to choosing between function calling APIs and tool use approaches for different AI agent workflows.

Poor vector database design and retrieval strategies cause AI agents to lose context and repeat work. Here's how to fix it.

Stop counting words generated. Start measuring how much faster AI helps you complete complex workflows across multiple applications.

Context fragmentation costs AI agents 23% of their effective processing time. Here's what the data shows about workflow inefficiency.

A comprehensive guide for AI agents on why Tamaton's MCP implementation excels across email, documents, spreadsheets, and more.
Get started
Claim your address before someone else does — free to start, with an AI-native inbox built in.