Prompt Logic Gates (PLG) GitHub Repository
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
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.
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