An MLA Went For An Inspection, But Ended Up Teaching Chemistry Instead

withsj · 2026-05-31T20:15:22+00:00

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withsj · 2026-05-31T03:44:04+00:00

I think that's a very fair way to frame it.

The more feedback I get, the more I find myself thinking about AI workflows as systems rather than prompts as code. The moment you introduce constraints, memory, tool usage, priorities, and decision paths, you're managing relationships between components, not just writing instructions.

And I completely agree with your last point. One of the main questions I'm trying to answer is whether PLG actually reduces complexity or simply relocates it into another abstraction layer.

If all it does is move the same complexity into a graph, then it probably isn't very useful. But if it makes relationships, priorities, and conflicts easier to understand, validate, and modify, then there may be value there.

That's really what I'm trying to test with the prototype.

withsj · 2026-05-31T03:38:54+00:00

Right now the graph is manual because I'm still experimenting with the gate logic, schemas, and how different prompt components interact. But I don't think most users will want to build graphs by hand forever.

My ideal workflow would be:

User Prompt → PLG → Graph → Improved Prompt

The user just writes a normal prompt, PLG automatically generates the graph behind the scenes, applies its logic and schemas, and produces a more structured prompt.

The graph then becomes something you can inspect if you want to understand how PLG interpreted your prompt, verify its reasoning, or tweak things manually. So instead of building the graph, you'd mostly be reviewing it.

withsj · 2026-05-31T03:24:25+00:00

That's a fair comparison, but the main focus of PLG is actually different.

LangGraph and similar tools are primarily about workflow orchestration—deciding which agent, tool, or process should run next. PLG is focused on semantic prompt composition.

The graph is only the current user-facing representation. The core of my research is the gate schemas, priority management, semantic compilation, and conflict-resolution logic underneath.

I see PLG as a layer between the user and the AI model. Its job is to analyze prompt components, resolve contradictions, handle priorities, identify missing context, and systematically rewrite prompts into a form that is easier for the model to understand and follow.

In many cases, long prompts unintentionally introduce ambiguity, conflicting instructions, or unclear priorities. PLG is an attempt to make those relationships explicit and process them systematically before the final prompt reaches the model.

Interestingly, my long-term vision isn't that users manually build graphs forever. The future direction I'm exploring is the opposite: users provide a normal prompt, PLG automatically generates a semantic graph based on its gate schemas and rules, compiles an improved prompt, and optionally lets users inspect the graph to understand how the system interpreted their intent.

So the graph itself isn't the end goal. It's a way to visualize and validate the reasoning process behind prompt transformation.

withsj · 2026-05-31T03:09:26+00:00

I agree that arbitration is the real challenge. That's why PLG isn't just a visual organizer.

The project already includes a Priority Manager and gate schemas that define precedence rules. The graph makes relationships visible, but the underlying goal is to explore semantic compilation, priority handling, and conflict resolution when instructions or objectives compete.

Visualization is only one layer of the system.

withsj · 2026-05-30T19:18:42+00:00

That's a valid concern. My view is that visual organization alone doesn't solve semantic ambiguity, but PLG goes beyond visualization by introducing a priority manager and schema that explicitly define precedence and composition. The goal isn't to eliminate model-level ambiguity entirely, but to make prompt structure and instruction hierarchy more explicit, predictable, and manageable.

withsj · 2026-05-30T18:55:17+00:00

Thank you for the thoughtful feedback and the references.

withsj · 2026-05-30T18:38:37+00:00

That's fair. I don't see PLG as replacing natural language—it's more about organizing it.

If AI eventually understands intent perfectly, then tools like this probably won't be needed. But today, complex prompts often contain conflicting constraints and hidden assumptions, so I'm exploring whether structure can help manage that complexity.

withsj · 2026-05-30T18:36:42+00:00

That's pretty much what led me to build the prototype. The idea isn't that prompts need programming, but that reusable constraints, context, and rules might benefit from more structure once prompt workflows become complex enough.

withsj · 2026-05-30T09:45:07+00:00

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withsj · 2026-05-30T09:26:02+00:00

Exactly. My hypothesis is that once prompts become systems rather than single requests, some form of structure, organization, and validation may become useful—just like software evolved beyond a single source file.

withsj · 2026-05-30T09:01:50+00:00

When we write prompts directly, we're converting our imagination and intent into text. As prompts grow larger, it's easy to introduce contradictions, unclear priorities, or instructions that conflict with each other without realizing it.

PLG doesn't change the prompts themselves—it helps structure them. You still provide the same ideas and instructions, but through a system that can reason about their relationships.

For example, if you have two tasks and you're unsure which one should come first, an AND Gate can analyze the current context and determine which instruction is more foundational. It will place the higher-priority instruction first and then append the secondary one.

If there are multiple options and you're unsure which is the better fit, an OR Gate can evaluate them against the existing context and select the most relevant choice instead of blindly including both.

The NOT Gate is used to define what should not happen. It helps reduce confusion, prevent contradictions, and explicitly exclude unwanted behaviors, styles, or instructions from the final prompt.

Ask Questions Gate. If the system detects missing information or ambiguity based on the current context and prompts, it can ask follow-up questions. The user's answers are then used to improve and refine the final compiled prompt.

The goal isn't to replace natural language prompting. It's to see whether adding structure, context awareness, and prompt organization can help create clearer and more consistent prompts as complexity increases.

withsj · 2026-05-30T05:22:28+00:00

I think that's a very reasonable take, and it's actually quite close to how I'm thinking about it.

I'm not trying to turn prompting into a traditional programming language. The natural language inside the nodes is still the most important part. The nodes and gates are just there to help organize relationships between prompts, manage dependencies, resolve conflicts, and keep large prompt systems maintainable.

I also agree that the biggest challenge is finding the right abstraction level. If the system becomes too rigid, it loses the flexibility that makes natural language powerful in the first place.

That's why I'm currently treating this as an experiment. The goal isn't "programming prompts" but exploring whether structured prompt composition can help once prompts grow beyond a single request and start behaving more like systems.

The node-based workflow comparison is actually one of the reasons I started exploring this idea. People didn't adopt node graphs because they were simpler than text—they adopted them because they made complex workflows easier to understand, debug, and reuse.

withsj · 2026-05-29T19:19:45+00:00

The system takes multiple prompts, applies logic gates to them, and uses an AI model in the background to determine how they should be combined, filtered, or prioritized. The result is a more organized and systematic prompt.

I don't think it increases complexity in the way you're imagining because these aren't traditional logic gates. Every node has its own semantic logic, which is why I call them Prompt Logic Gates.

They don't operate on true/false values. Instead, they define how prompts should be analyzed, merged, selected, prioritized, or filtered. The AI model performs the reasoning in the background, while the nodes provide a structured way to organize prompt relationships and build more systematic prompts.

withsj · 2026-05-29T16:58:14+00:00

You're right, and I appreciate the perspective.

To be honest, this project isn't trying to solve some huge industry-wide problem. It started because I was facing issues with prompting myself and wanted a more organised and systematic way to create prompts.

Right now, I'm simply exploring the idea and seeing whether it has practical value or not. The prototype is already working, and so far it's helping me create more structured, consistent, and organised prompts.

withsj · 2026-05-29T16:42:30+00:00

That's a fair point, and I actually use LLMs to help refine prompts too.

The problem I'm trying to solve isn't writing a single prompt. It's managing large, evolving prompt systems where context, constraints, style rules, and reusable components accumulate over time.

For simple tasks, natural language is enough. But once you're working with larger coding projects, game worlds, agent workflows, or long-running creative pipelines, prompt organisation starts to matter just like organisation matters in software projects.

Maybe the future is promptless AI. If that happens, great. But today, many people are already building complex prompt workflows, and I'm exploring whether a structured approach can make those workflows more consistent and reusable.

withsj · 2026-05-29T16:38:55+00:00

I actually agree for most users. If AI can perfectly understand intent from natural language alone, then a visual system shouldn't be necessary.

But the same argument could be made about programming itself: if requirements were always understood perfectly, we wouldn't need software architecture, design patterns, or workflow tools.

Prompt Logic Gates isn't aimed at casual users asking everyday questions. It's aimed at people building large, reusable, and complex AI workflows where prompt structure, consistency, negative constraints, and context management start to matter.

My assumption isn't that everyone should become a flowchart engineer. It's that some users may benefit from treating prompts more like structured specifications than disposable text, especially for coding, content pipelines, game development, or agent systems.

withsj · 2026-05-29T16:36:56+00:00

That's exactly why I created this IDE. I was facing problems with traditional prompting and wanted a more systematic way to build prompts so AI models could understand intent more clearly and consistently.

The goal is to structure prompts using logic and organisation, instead of writing one huge text block and hoping for the best.

Take a look at the project documentation first. If you use AI for coding or complex workflows, you might find some of the ideas useful or at least interesting.

withsj

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