If Your LLM Has No Guardrails, You Don't Have a Product

Three Guardrail Classes

Input guardrails: protect privacy, reject off-topic abuse, and limit oversized uploads.

Inference guardrails: structured output modes, controlled randomness per task, and rubric-grounded context.

Output guardrails: format validation, band-range checks, mandatory disclaimers, and blocklists for official-exam impersonation language.

Output Policy (Conceptual)

{
  "output_policy": {
    "must_include": ["band_estimate", "criteria_breakdown", "actionable_fixes"],
    "must_never_say": ["guaranteed band", "official examiner", "IDP endorsed"],
    "on_low_confidence": "ask_learner_to_retry_with_clearer_input",
    "label": "practice_estimate_not_official_score"
  }
}

Reliability Is the Product Surface

Users don't experience your base model — they experience the guardrail stack. A powerful model with weak guardrails loses to a focused model with rigorous policies in high-stakes domains like test prep.

This is why Band9AI is built as reliability infrastructure first, chat second.