Disclaimer: English is not my first language. I used language models (Claude sonnet 4, GPT-4o) to help edit grammar and clarity after writing my ideas, and AI tools (OpenAI Deep Search, Google NotebookLM) for research assistance. All core concepts, experiments, and technical work are my own. Unedited AI content is marked in collapsible sections.

TL;DR This article challenges the dominant "instruction-following assistant" paradigm in prompt engineering. Instead, I explore the possibility to interact with language models as navigators of semantic space—focusing on placing strategic coordinates rather than issuing detailed instructions.

Key insights

Human insight follows a predictable neural pattern: expand possibilities, filter noise, then bind distant concepts together
Language models appear to use a remarkably similar "expand-gate-bind" mechanism in their feed-forward networks
Effective prompting may work better as semantic navigation (providing sparse coordinates for circuit activation) rather than verbose instruction-giving
This could explain why unconventional prompting techniques work: they activate the right constellation of features in the model's latent space
I introduce Prompt Rover, a tool for visualizing these semantic navigations through black-box analysis (with significant limitations)

The core argument: we might improve human-AI collaboration by developing "latent space literacy"—learning to navigate semantic spaces through minimal, precise markers rather than detailed commands. This requires developing new intuitions about how concepts activate and combine in model inference.

Introduction

What if our interaction with language models is less like programming a computer and more like collaboratively tending a Zen garden—where each prompt is a rake stroke that influences the entire pattern, and understanding emerges from the interplay between human intuition and model geometry?

The dominant paradigm in prompt engineering treats language models as instruction-following assistants. We're told to "be clear, detailed, and complete"—to leave nothing to chance. But this framing may be limiting our effectiveness.

This article argues that we might achieve better results by learning to navigate semantic space alongside these models. When humans have an "Aha!" moment, our brains temporarily expand their search space, filter out irrelevant information, then ...

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