You’re not stuck because you can’t code fast enough.
You’re stuck because your workflow thinks in chaos, while your systems demand clarity.
In this GLNCD.IO mini-lesson, RAD² X shows you how Symbolic AI for coding workflows turns your development process from “AI autocomplete” into a deterministic, explainable architecture partner — especially when you’re shipping real products, not just scripts.
Instead of throwing vague prompts at a black-box model, you’ll see how to structure work as:
• Structured Inputs – Scope, Variables, Procedure, Guardrails
• Symbolic Reasoning – IF / THEN rules and logic graphs
• Deterministic Outputs – code, tests, and artifacts with a clear decision trail
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What You’ll Learn
In this video, you’ll learn how to:
• Move beyond “AI that guesses the next token” to symbolic co-architecture
• Capture requirements as symbols and rules instead of fuzzy tickets
• Use a three-stage pipeline:
• Input → Symbolic Layer → Deterministic Engine → Output
• Run a Beginner Workflow for single features or refactors
• Run a Professional Workflow for multi-service, multi-team systems
• Track metrics and pitfalls so your AI-assisted workflow stays transparent and reliable
Developers, tech leads, and non-technical innovators can use this to:
• Reduce ambiguity before coding
• Align AI-generated code with real constraints and guardrails
• Create living documentation that ties behavior back to explicit logic
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How to Use the RAD² X AI Agent via GLNCD.IO (Instructions Included)
You can turn this video into a symbolic dev assistant by configuring the RAD² X AI Agent via GLNCD.IO inside any advanced AI platform you use.
1. Define the Agent Role
In your agent’s system/instructions, set:
“You are the RAD² X AI Agent via GLNCD.IO.
Your job is to help me design and maintain symbolic, deterministic coding workflows.
Always structure my ideas into: Scope, Variables, Procedure, Guardrails, and IF / THEN Rules before generating or modifying code.”
2. Use It at the Start of Every Task
Before you code, prompt:
“RAD² X AI Agent via GLNCD.IO, here’s the feature or refactor I want.
1. Extract Scope, Variables, Procedure, Guardrails.
2. Propose IF / THEN Rules as a symbolic model.
3. Only after I confirm, suggest code changes and tests aligned with that model.”
3. Use It for Debugging & Audits
When something breaks or behaves weirdly:
“Show me the symbolic model behind this behavior.
Highlight where the rules and the actual outcome don’t match.
Propose updated IF / THEN rules and the minimal code changes required.”
Now your agent isn’t just writing lines — it’s maintaining a symbolic spec that you can inspect, refine, and trust.
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CTAs
If this shifted how you think about AI in development:
• Subscribe for more GLNCD.IO / RAD² X lessons on symbolic AI, system design, and coding workflows.
• Save this video as a reference before your next refactor or feature build.
• Share it with a developer or founder who’s excited about AI but cares about control and transparency.
• Comment below:
• Where would symbolic AI help you most: specs, refactors, tests, or audits?
For full articles, templates, and deep dives on How Symbolic AI for Coding Workflows Improves Development:
👉 Visit GLNCD.IO and explore the GLNCD.IO Knowledge Center.
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