Disclaimer: I’m a self-directed prompt engineering student and practitioner, not an academic or professor. With significant collaboration from Gemini, I’ve developed a structured course focused on advanced reasoning architectures in natural language.
The Core Problem
Foundational AI labs are pouring massive resources into test-time compute, reasoning models, and agentic harnesses. However, there is a distinct adoption bottleneck: most users struggle to structurally prompt these frontier models for complex, multi-step workflows without falling back on fragile, imperative application code or heavy external orchestration frameworks.
The Thesis: Natural Language as a Typed Intermediate Representation
Instead of treating prompts as soft conversational prose or retreating into rigid wrapper code prematurely, this curriculum treats the LLM context window as a formal execution runtime:
- Structural Scaffolds: Utilizes Context-Free Grammars (EBNF/BNF), schema contracts, Dynamic Skeleton-of-Thought (D-SoT), and state-space evaluation.
- Core Invariant Graph: Organizes 22 methods across 5 tiers (Epistemic Gating → Structural Decomposition → Non-Linear Search → Reflexive Verification → Dynamic Execution & Lifecycle).
- Durable Logic: Grounded in the principle that “syntax decays, logic compounds”—creating cognitive structures that remain portable across model generations.
Resources & Course Links
- Public Course Notebook:
The “Natural Language over Code” Lesson - Architectural Prompting Strategy:
Architectural Prompting for Reasoning Models
I’d love to hear how other developers and prompt engineers in the community are handling structured intermediate representations and state management natively in natural language!