Design an LLM ad copy system with human-in-the-loop
WHAT IT TESTS: Architecture for fine-tuning, guardrails, and human feedback loops. ANSWER OUTLINE: LoRA on approved copy, inference guardrails, human review, feedback as preference pairs for RLHF. RED FLAG: Treating review as static gate, not training signal.
WHAT IT TESTS: Whether you can architect a production generative system that marries parameter-efficient fine-tuning, inference guardrails, and continuous human feedback. ANSWER OUTLINE: Fine-tune a base model with LoRA on historically approved copy, add inference guardrails for brand safety, route outputs through a human review UI where editors approve or edit, and feed those decisions back as preference pairs for RLHF or reward-model updates.
Read the original → adp.xindoo.xyz
- #llm
- #fine-tuning
- #human-in-the-loop
- #ml-system-design
- #generative-ai
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