
Fine-Tuning vs Prompting for Knowledge Work in 2026
LoRA and lightweight fine-tuning are cheaper than ever, but for most inbox and document tasks, prompting plus retrieval still wins. Here's the decision tree.

LoRA and lightweight fine-tuning are cheaper than ever, but for most inbox and document tasks, prompting plus retrieval still wins. Here's the decision tree.

Calling AI agents 'digital employees' quietly erodes human accountability. Treat them as orchestrated, audited tools instead — here's how and why.

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.

A rigorous time-and-error log of AI in real knowledge work — separating genuine wins from rework and the hidden review tax.

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

A concrete audit of where your prompts, files, and context actually travel across AI assistants — and the data boundaries every enterprise should demand.

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

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.
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