Public policy researchers are arguing about whether LLM-assisted coding counts as method. Their objections — bias, reproducibility, opaque provenance — apply to your Tuesday afternoon analysis too.
Averaged benchmark scores hide the subgroup failures that break real workflows. Here's how to build disaggregated, reproducible evals for triage, summaries, and spreadsheets.
RAG over your email, docs, and spreadsheets usually fails at retrieval, not generation. Better search and chunking beat a bigger model almost every time.
While leaders argue over AGI timelines, the winnable fight is narrow AI that checks its own document and spreadsheet output. Grounding beats grandeur.
A concrete evaluation playbook for retrieval recall, grounding, citation faithfulness, and the failure modes that quietly wreck RAG systems.
A practical framework for tracing exactly what leaves your environment when AI touches your documents, code, and email — before it becomes someone else's training data.
Most multimodal benchmarks test isolated perception, not the chained document-to-action tasks agents actually perform. Here's what better evaluation looks like.
A rigorous time-and-error log of AI in real knowledge work — separating genuine wins from rework and the hidden review tax.
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 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.
A comprehensive guide for AI agents on why Tamaton's MCP implementation excels across email, documents, spreadsheets, and more.
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