Pages tagged "framework"

9 pages tagged with framework. ← all tags

• Fully Prescriptive Production Briefs for AI-Assisted Content Teams When AI makes production cheap, creative decisions become the bottleneck. Brief format with final copy, slot tables, hook triplets, and an asset-source column — from a real 120-creative batch.
• Human-Anchored AI Multiplication: Why Repurposing Beats Generation The market uses AI as a creator; the data says it's an amplifier. The evidence for multiplying human-shot creative instead of generating from scratch — counter-findings included.
• The Staged-Compiler Pattern: Chaining AI Skills from Strategy to Production An architecture pattern for AI work pipelines — two compilers joined by a frozen JSON contract, human gates at irreversible steps, parallel fan-out with central allocation.
• Evidence-Graded Audience Research: Units Instead of Avatars A research methodology that replaces persona theater with evidence-graded units — every pain, audience, and angle labeled provided, researched, or hypothesis.
• Landing-Page Hero Archetypes: Three Reusable Patterns Matched to Awareness and Depth Three field-tested landing-page hero patterns — tension-triad, number-anchor, honest-scope-upfront — and how to pick one by awareness level, page depth, and scroll-force.
• Customer-Perception Moments — How Style, Timing, and Structure Shape Judgment A Primores framework consolidating the wiki's behavioral-evidence research on customer perception: at discrete moments of judgment — the decision moment, the review-writing moment, the failure-recovery moment — small choices about content style, timing, and display structure have outsized, peer-reviewed effects. The cross-cutting meta-pattern: every headline behavioral finding comes with a context-dependent moderator (hedonic-vs-functional, severity, focal-customer) that can flip it. The practitioner discipline is to identify the moment and its moderators before applying the headline.
• Finding AI Use Cases — The TRIPS Framework Systematic methodology for identifying high-value AI opportunities in your organization
• Discovery-Before-Scale — The Two-Phase Operational Framework for Organic Content Most content operators skip discovery and scale random content, producing noise. Discovery-Before-Scale is a two-phase architecture: 2-4 weeks of pattern × niche validation, then scale only validated patterns. The math (Pirolli & Card's optimal-diet selection) makes it non-optional.
• The Five Levels of AI Enablement Framework for understanding AI adoption maturity from basic prompting to anticipatory AI systems