---
id: "prereq-domain-knowledge"
type: "prereq"
source_timestamps: ["§ Step 3. Analyze the differences between your initial view (from Step 1) and AI's output.", "§ Making Judgment Teachable"]
tags: ["expertise", "context"]
related: ["concept-looks-right-but-isnt"]
reason: "Required to detect plausible but contextually incorrect AI outputs."
sources: ["reskilling"]
sourceVaultSlug: "hbr-seg-reskilling"
originDay: 10
articleStem: "hbr-edu-32-help-employees-get-better-with-ai"
sourceUrl: "https://hbr.org/2026/06/help-employees-get-better-not-just-faster-with-ai"
sourceTitle: "Help Employees Get Better—Not Just Faster—with AI"
---
# Underlying Domain Knowledge

**Prerequisite:** Underlying domain knowledge (and access to non-public context).

To successfully execute [[framework-four-step-ai-development|Step 3]] ([[framework-difference-analysis|difference analysis]]) and catch outputs that [[concept-looks-right-but-isnt|look right but aren't]], the professional must possess a baseline of domain expertise plus access to non-public context — e.g., unannounced regulatory changes or proprietary research data.

**Why it's required:** Without this, the human cannot serve as an effective check on the AI's plausible hallucinations. The enrichment overlay's counter-perspective notes this makes the model potentially *unequal in practice* — it may advantage already-strong performers and leave novices [[question-junior-employee-baseline|struggling to form a credible initial POV]] [7].