· 4 min read
RAG Isn't Dead: When Retrieval Beats a Bigger Context Window
A practitioner's framework for choosing retrieval-augmented generation over long-context prompting — based on cost, freshness, and grounding, not vibes.
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
RAG Is Not Search, and Your Inbox Proves It
Retrieval-augmented generation quietly breaks on email and files because it ranks documents, not conversations. Here's why, and what to do about it.
· 4 min read
How to Evaluate a RAG System Before You Trust It
A concrete evaluation playbook for retrieval recall, grounding, citation faithfulness, and the failure modes that quietly wreck RAG systems.
· 4 min read
How Tamaton Grounds Agent Actions in Your Real Data
A concrete look at how shared context across inbox, docs, sheets, and calendar gives AI agents verifiable grounding — and fewer confidently wrong actions.
· 4 min read
Can LLMs Predict Human Behavior? What the Research Says
New studies suggest LLMs can forecast social-science and neuroscience results. Here's what that actually means for knowledge work — and what it doesn't.
· 5 min read
LLMs Beat Experts at Predicting Study Results. Now What?
A study found LLMs can forecast experiment outcomes better than human specialists. We unpack what that really proves — and how to use it without fooling yourself.
· 4 min read
Give Your Agent a Login: Custom Agents in Tamaton
A concrete walkthrough of building a scoped Tamaton agent that triages your inbox, drafts replies, and files documents — with its own identity and permissions.
· 4 min read
RAG for Your Inbox: Retrieval Over an Email Corpus
Email is the hardest RAG target there is — threads, quotes, and recency all conspire against you. Here's how to design inbox retrieval that's actually accurate.
· 4 min read
Context Windows Are a Trap: Why RAG Still Beats 1M Tokens
Frontier models keep bragging about million-token context windows. For real knowledge work, disciplined retrieval still wins on accuracy, cost, and latency.
· 4 min read
From Chat to Workflows: What Makes a Stack Agent-Ready
Agent-readiness isn't about bolting on chatbots. It's about exposing structured tools, clean state, and observable actions across email, docs, and search.
· 4 min read
Model Selection for Agents: Match the Model to the Task
One model can't optimally draft, extract, schedule, and search. Here's a practical routing framework for choosing and switching models per task.
· 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.
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