
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.

Stuffing a million tokens into a prompt degrades reasoning more than it helps. The real skill is curating what the model actually attends to.

A practical decision framework for choosing retrieval, full-context, or hybrid approaches based on data volatility, cost, and accuracy.

When agents read and draft your email, success isn't an empty inbox — it's well-designed triage rules, escalation thresholds, and approval boundaries.

Email threads encode decisions, commitments, and relationships better than any wiki — making your inbox the highest-signal grounding source for an AI assistant.

Practical patterns for coaxing consistent, spreadsheet-ready data out of LLMs — schema enforcement, validation loops, and the pitfalls that quietly corrupt your tables.

Pure vector search over personal files stumbles on recency, permissions, and exact terms. Hybrid retrieval plus metadata is the fix.

Final-answer accuracy hides how agents actually work. Here's why trajectory, tool-use, and recovery metrics matter — plus a practical scoring rubric.

A practical framework for routing tasks to small, fast models or frontier reasoning models based on latency, cost, and failure cost.

A quantitative look at how context window limits quietly degrade agent performance on email threading and document synthesis—and what to do about it.

Stop defaulting to the largest frontier model. Use a cost-latency-quality matrix to route each task to the right LLM and cut spend without hurting output.

A concrete eval rubric for email AI — measuring intent precision, action safety, and hallucinated commitments — plus test cases you can run today.

Frontier models aren't always the answer. For inbox and search work, routing to small fine-tuned models is quietly becoming the default architecture.
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