The article proposes treating AI prompts as executable logic using visual nodes and logic gates, similar to visual programming languages, and introduces a prototype called Prompt Logic Gates (PLG).
[Prompt Logic Gates (PLG) GitHub Repository](https://github.com/WithSJ/Prompt-Logic-Gates-PLG/tree/main?utm_source=chatgpt.com) Something I've been thinking about recently. In software development, we've spent decades building abstractions to make complex systems manageable: * Functions instead of repeating code * Classes and modules instead of giant files * Visual systems such as Unreal Blueprints, Node-RED, and LabVIEW. * Compilers that validate and transform input before execution But when it comes to AI prompts, many of us are still writing massive text blobs. A complex prompt can easily become hundreds of words long with multiple responsibilities: * Context * Constraints * Style instructions * Exclusions * Decision logic * Fallback behavior At that point, it starts feeling less like text and more like a program. That made me wonder: Why don't we treat prompts as executable logic? Imagine building prompts using logic gates: * AND → merge instructions * OR → choose between alternatives * NOT → remove unwanted concepts * Question nodes → identify missing requirements * Compiler → validate contradictions before execution Instead of editing a giant string, you'd build a graph and compile it into the final prompt. I've been experimenting with this idea in a prototype called **Prompt Logic Gates (PLG)**. It treats prompts like compilable programs, using concepts such as dependency graphs, execution order, semantic conflict detection, visual nodes, and compilation pipelines. such as Unreal Blueprints, Node-RED, and LabVIEW Repo: [Prompt Logic Gates (PLG) GitHub Repository](https://github.com/WithSJ/Prompt-Logic-Gates-PLG/tree/main?utm_source=chatgpt.com) I'm not posting this as a product launch or anything — I'm more interested in whether this direction makes sense from a software engineering perspective. Do you think prompts eventually become a programming layer of their own? Or will natural language always be the better abstraction? Curious what other developers think.
Prompt Logic Gates (PLG) is a visual prompt engineering experiment that organizes prompts using semantic logic gates (AND, OR, NOT, Ask Questions) to manage complex system-like prompts, aiming to improve maintainability and consistency.
The article argues that reliable AI agents require deterministic control flow and programmatic verification in software, rather than relying solely on complex prompt chains.
The article explains the concept of 'loops' in AI coding, where developers write programs that prompt coding agents instead of manually prompting, as popularized by Peter Steinberger and Boris Cherny, and discusses how this shift represents a new abstraction layer in AI-assisted development.
Garry Tan advocates shifting from manual AI prompting to skill-based automation, presenting open-source tools like GBrain and GStack to capture and reuse workflows permanently.
This article offers tips for crafting effective AI prompts using the Vibe Coding feature in Google AI Studio, highlighting the importance of specificity, the use of keywords such as Three.js, image references, and iterative refinement.