Technicolour II: the videoclip

This Intermediate Guide for the ONHT (Objective, Needs, How, Trajectory) Framework transforms you from someone who uses GenAI into someone who thinks with GenAI by adding the missing cognitive functions that current GenAI lacks. The framework works through three critical pillars – Empathy (understanding all stakeholders), Critical Thinking (challenging assumptions), and Human in the Loop (active partnership). Master these patterns and you'll be solving complex problems others can't even approach, becoming indispensable by designing interactions that produce exceptional results rather than just functional outputs.

Stop getting generic AI responses. Learn the four-letter framework that transforms vague requests into precise results. The ONHT framework: Objective (what problem you're solving), Needs (key information that matters), How (the thinking approach), and Trajectory (clear steps to the answer), teaches you to think WITH AI, not through it, turning "analyse customer feedback" into board-ready insights. Real examples show how adding context and structure gets you from Level 1 basics to Level 3 mastery, where AI delivers exactly what you need.
The difference? Knowing how to ask.

GenAI tools are transforming work, but most people get poor results because they don't understand how to communicate with AI built on structured data. This guide is a series of articles that teaches the ONHT framework—a systematic approach to prompting that transforms vague requests into exceptional outputs by focusing on Objectives (what problem), Needs (what information), How (thinking approach), and Trajectory (path to solution). Master this framework and develop an expert mindset grounded in human-in-the-loop thinking, critical analysis, and empathy, and you'll excel with any AI tool, at any company, in any role.

I question the reliability and accuracy of Generative AI (GenAI) in enterprise scenarios, particularly when faced with adversarial questions, highlighting that current Large Language Models (LLMs) may be data-rich but lack in reasoning and causality. I would call for a more balanced approach to AI adoption in cases of assisting users, requiring supervision, and the need for better LLM models that can be trusted, learn, and reason.