Turning ChatGPT into a Deterministic Flight-Risk Runtime (FRR Demo + GitHub Repo)
Most people treat ChatGPT as a conversational model.
I wanted to know what happens if you force it to behave like a deterministic execution engine instead.
To test this idea, I built a miniature Flight Readiness Review (FRR) Runtime that runs entirely inside ChatGPT —
no API, no tools, no plugins, no backend — just structure and constraints.
And surprisingly, it works extremely well.
🚀 Why Build a Deterministic Runtime Inside an LLM?
LLMs are fuzzy by nature:
- They improvise
- They drift
- They sometimes hallucinate
So I wanted to push them to the opposite extreme:
Can an LLM execute a deterministic pipeline with reproducible outputs
even in a free-form chat environment?
The answer is yes, as long as the structure is strong enough.
🧠 What the FRR Runtime Actually Does
The FRR Runtime processes a structured telemetry block
(winds, pressure, pump vibration, IMU drift, etc.)
and performs an 8-step deterministic reasoning loop:
- Parse input
- Normalize variables
- Factor Engine (F1–F12)
- Global RiskMode
- Subsystem evaluation
- KernelBus arbitration
- Counterfactual reasoning
- Produce a strict
FRR_Resultblock
No chat.
No narrative.
No deviation.
Same input → same output.
📡 Real-Case Replay Tests (Not Simulations)
To test stability, I ran the runtime against several well-known launch scenarios:
- ❄ Cold O-ring resilience failure (Challenger-style) → clear NO-GO
- 🔥 COPV thermal instability (AMOS-6-style) → NO-GO
- 🌬 High wind shear with stable propulsion → HOLD
The point is not aerospace accuracy —
the point is that the LLM stayed deterministic,
followed the pipeline, and never drifted.
🎥 Demo Video (3 minutes)
Here is the FRR Runtime running in the ChatGPT client:
https://youtu.be/9R6wc-LVzSc
📦 GitHub Repo
Including the soft-system prompt, full FRR specification, and sample telemetry inputs:
https://github.com/yuer-dsl/qtx-frr-runtime
🔍 Why This Matters Beyond This Demo
This experiment suggests something important:
LLMs can operate as deterministic runtimes
if given enough structural constraints.
This has big implications for:
- agent systems
- reproducible reasoning
- safety-critical assessment
- on-device AI runtimes
- deterministic / hybrid agents
- structured execution pipelines
- alternatives to tool-based agent frameworks
LLMs might behave more like components of an operating system
than we previously assumed.
📌 Final Thoughts
This FRR Runtime is not an aerospace tool.
But it is a working proof that:
- structure → determinism
- determinism → reproducible reasoning
- reproducible reasoning → safer agents
If you’re exploring deterministic AI behavior, structured LLM runtimes,
or alternative agent architectures, this experiment might interest you.
More deterministic runtimes coming soon (medical risk, financial risk, etc.).
⭐ Want the Soft-System Prompt?
If anyone wants the FRR Runtime soft prompt (safe, stripped-down version)
I’m happy to share it in the comments.
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