· 4 min read
How to Evaluate an LLM for Your Workflow (Not the Leaderboard)
Public benchmarks rarely predict real performance. Here's how to build a task-specific eval harness from your own emails, docs, and spreadsheets.
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#search#security#spreadsheets#startups#storage#tamaton#unified-workspace#workflow#workflows
· 4 min read
Context Windows Are Not Memory: Designing Agent State
The context window is a scratchpad, not storage. Here's how to architect external memory layers for durable, reliable agent state.
· 4 min read
Why Your RAG Pipeline Retrieves the Wrong Chunks
A diagnostic framework for the quiet retrieval failures that degrade RAG quality — from chunking strategy to embedding mismatch.
· 4 min read
Why Your RAG Pipeline Retrieves the Wrong Chunks
Retrieval failures aren't one bug — they're three. A diagnostic framework for isolating chunking, embedding, and reranking problems instead of guessing.
· 4 min read
Email Triage at Scale: Building Stateful Classification Systems
An architecture guide for AI systems that classify email by learning from patterns over time, rather than judging each message in isolation.
· 4 min read
The Inbox Is a Database: Rethinking Email for Agents
Stop treating email as a message stream to triage. For agents, the inbox is queryable state — structured, indexed, and ready to act on.
· 4 min read
Choosing an LLM by Task, Not Benchmark
Leaderboard scores rarely predict production performance. Here's a decision framework that maps real workloads to the right model.
· 5 min read
Evaluating LLM Output Without a Golden Dataset
Practical LLM evaluation methods for teams without labeled ground truth: LLM-as-a-judge, rubric scoring, and regression sets you can ship today.
· 4 min read
Prompt Caching Strategies for Multi-Agent Workflows
A practical guide to building prompt caching layers that cut latency and cost across complex multi-agent orchestrations.
· 5 min read
Context Windows Are Lying to You: The Lost-in-the-Middle Tax
Bigger context windows don't guarantee better recall. Here's where models actually lose information — and how to structure prompts so they don't.
· 4 min read
Email Triage With LLMs: Classification Beats Generation
The highest-ROI AI in your inbox isn't drafting replies — it's routing, prioritizing, and summarizing. Here's the architecture to build it.
· 4 min read
Calendar Optimization: Tamaton's Constraint Solver for AI Scheduling
A technical look at how Tamaton models multi-party scheduling as a constraint satisfaction problem to coordinate meetings across AI agents and humans.
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