5 Prompting Tricks to Make Your AI Less Average

Quick Overview

To make a Large Language Model (LLM) less average and achieve high-quality, distinct output, users must move beyond simple prompting and implement strategies like using a "Negative Style Guide" to explicitly tell the model what to avoid, such as common clichés, telemetric language, or specific formatting like colons and dashes, as demonstrated by the speaker's successful use of these techniques with both GPT-5 and O3 models.

Key Points: LLMs trained on the entire corpus of human output suffer from an AI sameness problem, producing average outputs. The speaker advocates for a "Negative Style Guide" as a core technique to make LLM output non-average and distinct. This technique involves explicitly instructing the LLM what to avoid, such as clichés, telemetric language, or specific formatting like colons and dashes. The speaker successfully used this method with both GPT-5 and O3 models to generate superior outputs compared to standard prompting. A key goal is to force the model to choose between divergent paths rather than defaulting to the average consensus, exemplified by asking it to argue for one option over another. The speaker notes that the O3 model struggled to avoid certain patterns, requiring constant reminders, while the newer GPT-5 thinking was better at incorporating these constraints. A related technique is "Self Critique," where the model is asked to critique its own output using examples to explain why the consensus view is wrong.

Context: The video addresses the common issue where AI-generated content, having been trained on the entire human corpus, often defaults to producing average, uniform, or cliché-ridden output. The speaker frames this as AI's "tyranny of the average," arguing that achieving truly unique and high-quality results requires intentional deviation from this baseline. The discussion focuses on practical prompting strategies developed through the speaker's personal experience using models like GPT-5 and O3.

Detailed Analysis