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The ONHT Framework for Intermediate users

By John Garner on Wednesday, June 18, 2025

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.

The ONHT Framework: Beginners Guide

By John Garner on Monday, June 16, 2025

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.

The ONHT Framework: GenAI Prompting Solutions That Actually Work for People

By John Garner on Sunday, June 15, 2025

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.

The reliability & accuracy of GenAI

By John Garner on Sunday, September 24, 2023

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.

From Chatbots to Reducing Society's Technical Debt

By John Garner on Saturday, September 23, 2023

I discuss my experience with chatbots, contrasting older rules-based systems with newer GenAI (General Artificial Intelligence) chatbots. We cannot dismiss the creative capabilities of GenAI-based chatbots, but these systems lack reliability, especially in customer-facing applications, and improvements in the way AI is structured could lead to a "software renaissance," potentially reducing society's technical debt.

The imbalance of power in the AI game: in search of the common good

By John Garner on Friday, June 16, 2023

The article discusses the contrasting debate on how AI safety is and should be managed, its impact on technical debt, and its societal implications. It notes the Center for AI Safety's call for a worldwide focus on the risks of AI, and Meredith Whittaker's criticism that such warnings preserve the status quo, strengthening tech giants' dominance. The piece also highlights AI's potential to decrease societal and technical debt by making software production cheaper, simpler, and resulting in far more innovation. It provides examples of cost-effective open-source models that perform well and emphasizes the rapid pace of AI innovation. Last, the article emphasises the need for adaptive legislation to match the pace of AI innovation, empowering suitable government entities for oversight, defining appropriate scopes for legislation and regulation, addressing ethical issues and biases in AI, and promoting public engagement in AI regulatory decisions.

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