
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.

Reasoning depth should scale with task difficulty. Here's when chain-of-thought helps, when it hurts, and how to spend reasoning tokens wisely.

The clever one-liner prompt is fading. Durable AI work needs specifications: constraints, examples, and acceptance criteria you can reuse.

A decision framework for choosing constrained AI workflows over open-ended agents — and the specific conditions that justify going agentic.

How to build evals for subjective knowledge work — emails, summaries, docs — using rubrics, pairwise comparison, and human-in-the-loop sampling.

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.

Public leaderboards reward the wrong things for agentic work. Here's a practical eval harness built around real task completion and side-effect safety.

Why LLMs should generate and execute formulas instead of hallucinating math — plus concrete patterns for verifiable spreadsheet automation.

Email looks routine, but it's the toughest environment for autonomy: ambiguous intent, irreversible sends, and tangled threading state. Here's what reliable inbox automation really takes.

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.
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