· 4 min read
RAG for Inboxes: Grounding Email Replies in Real Context
Email is a messy, threaded corpus. Good retrieval design — thread chunking, dedup, recency weighting — decides whether AI replies are trustworthy.
All#ai#ai-agents#ai-productivity#applied-ai#automation#calendar#context-switching#deep-work#documents#email#engineering#engineering-leadership#invisible-ai#knowledge-work#llm-evaluation#product-updates#productivity#prompt-engineering#remote-work#search#security#spreadsheets#startups#storage#tamaton#unified-workspace#workflow#workflows
· 4 min read
Why Multimodal Benchmarks Still Fail Agentic Work
Most multimodal benchmarks test isolated perception, not the chained document-to-action tasks agents actually perform. Here's what better evaluation looks like.
· 4 min read
How to Evaluate an Agent That Edits Your Spreadsheet
Spreadsheet edits demand cell-level, deterministic evaluation — accuracy, reversibility, and formula integrity — not vibes-based scoring.
· 4 min read
Why Agents Fail at Long Tasks (And How to Fix Drift)
A diagnostic framework for why agentic workflows degrade over multi-step tasks — context loss, tool errors, and goal drift — plus concrete mitigations.
· 4 min read
Difficulty-Aware Prompting: When Chain-of-Thought Hurts
Reasoning depth should scale with task difficulty. Here's when chain-of-thought helps, when it hurts, and how to spend reasoning tokens wisely.
· 4 min read
Prompting Is Becoming Spec-Writing
The clever one-liner prompt is fading. Durable AI work needs specifications: constraints, examples, and acceptance criteria you can reuse.
· 4 min read
AI Workflows vs. AI Agents: When to Use Which
A decision framework for choosing constrained AI workflows over open-ended agents — and the specific conditions that justify going agentic.
· 5 min read
Evaluating AI Output When There's No Right Answer
How to build evals for subjective knowledge work — emails, summaries, docs — using rubrics, pairwise comparison, and human-in-the-loop sampling.
· 4 min read
Why Most RAG Pipelines Fail at Retrieval, Not Generation
When a RAG pipeline gives bad answers, the LLM usually isn't the problem. Retrieval is. Here's where chunking, ranking, and recall actually break.
· 4 min read
Evaluating AI Agents: Why Task Success Beats Benchmarks
Public leaderboards reward the wrong things for agentic work. Here's a practical eval harness built around real task completion and side-effect safety.
· 4 min read
Spreadsheets as Tool Use: Letting LLMs Compute, Not Guess
Why LLMs should generate and execute formulas instead of hallucinating math — plus concrete patterns for verifiable spreadsheet automation.
· 4 min read
Context Windows Are Cheap, Attention Is Not
Stuffing a million tokens into a prompt degrades reasoning more than it helps. The real skill is curating what the model actually attends to.
Get started
Reserve your @tamaton.ai email
Claim your address before someone else does — free to start, with an AI-native inbox built in.