Most examples of LLM usage on DEV focus on one of three things:
Prompt tricks
Tool calling / agents
Backend-heavy workflows
This post explores a different idea:
What if a single GPT client could behave like a lightweight, auditable runtime — purely through interaction design?
No APIs.
No plugins.
No external services.
The problem: chat is a poor execution model
Chat-style prompting is flexible, but it has structural weaknesses:
Outputs vary across runs
Decisions are hard to audit
Missing inputs don’t block execution
For decision-oriented tasks (investment checks, risk screening, stop-loss decisions), this is a serious problem.
The issue is not intelligence.
It’s the lack of an execution contract.
The core idea: language-level runtime
In software, a runtime enforces three things:
Input contracts
Execution order
Output structure
Instead of building a new framework, I tried enforcing these constraints directly in natural language, inside a GPT client.
The result behaves surprisingly like a runtime.
Step 1: protocol binding (runtime header)
Every session begins with a minimal header:
protocol: yuerdsl
runtime: LSR
edition: Personal
This is not decoration.
If the header is missing, the system intentionally falls back to chat behavior.
Think of it as a language-level execution gate.
Step 2: strict input contracts (DSL as a form)
Users don’t “ask questions.”
They fill a structured template (YAML-style form).
Key rule:
No completed template → no decision output
This alone eliminates most hallucination-driven conclusions.
Step 3: fixed execution pipeline
Once the template is complete, the runtime executes a fixed pipeline:
Stage detection
State compilation
Structural risk analysis
Decision grading (PASS / WATCH / STOP)
Action list
Audit receipt
There is no branching logic exposed to the user.
Step 4: auditable output
Each run ends with an audit receipt containing:
Input digest
Key variables
Assumptions
Decision grade
Action priorities
This makes runs comparable and replayable.
Same input → same structure → same decision grade.
Isn’t this just prompt engineering?
Not really.
Prompt engineering optimizes what to say.
This approach constrains what is allowed to execute.
The model is not encouraged to be clever — it is required to be consistent.
Why not agents or tools?
Agents and tools are powerful, but they add complexity:
Tool failure modes
State synchronization
Backend dependencies
This experiment intentionally asks a narrower question:
How far can we go with zero infrastructure, using only protocol design?
For lightweight, client-only scenarios, the answer is: surprisingly far.
Why GPT (the client)?
This is an engineering choice, not brand preference.
At the moment, GPT offers:
Stable adherence to long structured instructions
Reliable parsing of form-like input
A consistent client-side execution environment
The approach itself is model-agnostic.
What this experiment shows
LLMs are probabilistic.
But their execution surface doesn’t have to be.
With strict contracts and refusal rules, you can get:
Repeatable decisions
Clear failure modes
Human-verifiable traces
That’s often enough for real-world use.
Final thought
Instead of asking:
“How do we make LLMs smarter?”
It may be more productive to ask:
“How do we make them accountable?”
Sometimes, better constraints beat bigger models.
Author: Yuer
Independent system architect
Focus: controllable AI, human–AI protocols, language-level runtimes
Project / DSL schemas
👉 https://github.com/yuer-dsl
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