Mustafa Darras · AI 与 LLM 评估总监
LLM 不做决策
2026-02-05 · 10 min
LinkedIn 摘要
LLMs predict text. They do not decide. At Band9AI, we harness them to provide accurate, reliable predictions — not guesswork. Proper guidelines and structured workflows ensure outputs are consistent, prevent hallucinations, and maintain high confidence.
技术深读
Prediction Layer vs Decision Layer
An LLM completion is a probability distribution over tokens. Calling that a 'decision' is a category error — and a liability error when users treat band estimates as official scores.
Band9AI splits the stack: the model proposes structured feedback; validators enforce format, range, and policy; only then does the UI present an estimate clearly labeled as practice, not examiner truth.
Scoring Workflow (Conceptual)
{
"workflow": [
{"step": "generate", "role": "AI proposes criterion-level feedback"},
{"step": "validate", "role": "Check format, band range, and policy compliance"},
{"step": "filter", "role": "Block official-exam impersonation language"},
{"step": "present", "role": "Show practice estimate with clear disclaimer"}
],
"principle": "AI suggests — the system decides what learners see"
}Why This Matters for IELTS Prep
Students don't need another chatbot that sounds authoritative. They need predictions bounded by public band descriptors, with visible uncertainty when the model is extrapolating.
When designed correctly, LLMs become engines for authoritative insights — not suggestions dressed as decisions.
实体锚点: Mustafa Darras · linkedin.com/in/mustafadarras · 全部架构笔记